Machine Learning

These dataclasses are used in the SDK to represent API requests and responses for services in the databricks.sdk.service.ml module.

class databricks.sdk.service.ml.Activity(activity_type: ActivityType | None = None, comment: str | None = None, creation_timestamp: int | None = None, from_stage: str | None = None, id: str | None = None, last_updated_timestamp: int | None = None, system_comment: str | None = None, to_stage: str | None = None, user_id: str | None = None)

For activities, this contains the activity recorded for the action. For comments, this contains the comment details. For transition requests, this contains the transition request details.

activity_type: ActivityType | None = None

comment: str | None = None

User-provided comment associated with the activity, comment, or transition request.

creation_timestamp: int | None = None

Creation time of the object, as a Unix timestamp in milliseconds.

from_stage: str | None = None

Source stage of the transition (if the activity is stage transition related). Valid values are:

• None: The initial stage of a model version.

• Staging: Staging or pre-production stage.

• Production: Production stage.

• Archived: Archived stage.

id: str | None = None

Unique identifier for the object.

last_updated_timestamp: int | None = None

Time of the object at last update, as a Unix timestamp in milliseconds.

system_comment: str | None = None

Comment made by system, for example explaining an activity of type SYSTEM_TRANSITION. It usually describes a side effect, such as a version being archived as part of another version’s stage transition, and may not be returned for some activity types.

to_stage: str | None = None

Target stage of the transition (if the activity is stage transition related). Valid values are:

• None: The initial stage of a model version.

• Staging: Staging or pre-production stage.

• Production: Production stage.

• Archived: Archived stage.

user_id: str | None = None

The username of the user that created the object.

as_dict() → dict

Serializes the Activity into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Activity into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Activity

Deserializes the Activity from a dictionary.

class databricks.sdk.service.ml.ActivityAction

An action that a user (with sufficient permissions) could take on an activity or comment. For activities, valid values are: * APPROVE_TRANSITION_REQUEST: Approve a transition request * REJECT_TRANSITION_REQUEST: Reject a transition request * CANCEL_TRANSITION_REQUEST: Cancel (delete) a transition request For comments, valid values are: * EDIT_COMMENT: Edit the comment * DELETE_COMMENT: Delete the comment

APPROVE_TRANSITION_REQUEST = "APPROVE_TRANSITION_REQUEST"

CANCEL_TRANSITION_REQUEST = "CANCEL_TRANSITION_REQUEST"

DELETE_COMMENT = "DELETE_COMMENT"

EDIT_COMMENT = "EDIT_COMMENT"

REJECT_TRANSITION_REQUEST = "REJECT_TRANSITION_REQUEST"

class databricks.sdk.service.ml.ActivityType

Type of activity. Valid values are: * APPLIED_TRANSITION: User applied the corresponding stage transition. * REQUESTED_TRANSITION: User requested the corresponding stage transition. * CANCELLED_REQUEST: User cancelled an existing transition request. * APPROVED_REQUEST: User approved the corresponding stage transition. * REJECTED_REQUEST: User rejected the coressponding stage transition. * SYSTEM_TRANSITION: For events performed as a side effect, such as archiving existing model versions in a stage.

APPLIED_TRANSITION = "APPLIED_TRANSITION"

APPROVED_REQUEST = "APPROVED_REQUEST"

CANCELLED_REQUEST = "CANCELLED_REQUEST"

NEW_COMMENT = "NEW_COMMENT"

REJECTED_REQUEST = "REJECTED_REQUEST"

REQUESTED_TRANSITION = "REQUESTED_TRANSITION"

SYSTEM_TRANSITION = "SYSTEM_TRANSITION"

class databricks.sdk.service.ml.AggregationFunction(approx_count_distinct: ApproxCountDistinctFunction | None = None, approx_percentile: ApproxPercentileFunction | None = None, avg: AvgFunction | None = None, count_function: CountFunction | None = None, first: FirstFunction | None = None, last: LastFunction | None = None, max: MaxFunction | None = None, min: MinFunction | None = None, stddev_pop: StddevPopFunction | None = None, stddev_samp: StddevSampFunction | None = None, sum: SumFunction | None = None, time_window: TimeWindow | None = None, var_pop: VarPopFunction | None = None, var_samp: VarSampFunction | None = None)

An aggregation function applied over a time window.

approx_count_distinct: ApproxCountDistinctFunction | None = None

approx_percentile: ApproxPercentileFunction | None = None

avg: AvgFunction | None = None

count_function: CountFunction | None = None

first: FirstFunction | None = None

last: LastFunction | None = None

max: MaxFunction | None = None

min: MinFunction | None = None

stddev_pop: StddevPopFunction | None = None

stddev_samp: StddevSampFunction | None = None

sum: SumFunction | None = None

time_window: TimeWindow | None = None

The time window over which the aggregation is computed.

var_pop: VarPopFunction | None = None

var_samp: VarSampFunction | None = None

as_dict() → dict

Serializes the AggregationFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the AggregationFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → AggregationFunction

Deserializes the AggregationFunction from a dictionary.

class databricks.sdk.service.ml.ApproveTransitionRequestResponse(activity: 'Optional[Activity]' = None)

activity: Activity | None = None

New activity generated as a result of this operation.

as_dict() → dict

Serializes the ApproveTransitionRequestResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ApproveTransitionRequestResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ApproveTransitionRequestResponse

Deserializes the ApproveTransitionRequestResponse from a dictionary.

class databricks.sdk.service.ml.ApproxCountDistinctFunction(input: str, relative_sd: float | None = None)

Computes the approximate count of distinct values.

input: str

The input column from which the approximate count of distinct values is computed.

relative_sd: float | None = None

The maximum relative standard deviation allowed (default defined by Spark).

as_dict() → dict

Serializes the ApproxCountDistinctFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ApproxCountDistinctFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ApproxCountDistinctFunction

Deserializes the ApproxCountDistinctFunction from a dictionary.

class databricks.sdk.service.ml.ApproxPercentileFunction(input: str, percentile: float, accuracy: int | None = None)

Computes the approximate percentile of values.

input: str

The input column from which the approximate percentile is computed.

percentile: float

The percentile value to compute (between 0 and 1).

accuracy: int | None = None

The accuracy parameter (higher is more accurate but slower).

as_dict() → dict

Serializes the ApproxPercentileFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ApproxPercentileFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ApproxPercentileFunction

Deserializes the ApproxPercentileFunction from a dictionary.

class databricks.sdk.service.ml.AuthConfig(mtls_config: 'Optional[MtlsConfig]' = None, uc_service_credential_name: 'Optional[str]' = None)

mtls_config: MtlsConfig | None = None

Mutual-TLS authentication. See MtlsConfig.

uc_service_credential_name: str | None = None

Name of the Unity Catalog service credential. This value will be set under the option databricks.serviceCredential

as_dict() → dict

Serializes the AuthConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the AuthConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → AuthConfig

Deserializes the AuthConfig from a dictionary.

class databricks.sdk.service.ml.AvgFunction(input: str)

Computes the average of values.

input: str

The input column from which the average is computed. For Kafka sources, use dot-prefixed path notation (e.g., “value.amount”). For nested fields, the leaf node name is used. TODO(FS-939): Colon-prefixed notation (e.g., “value:amount”) is supported for backwards compatibility but is deprecated; migrate to dot notation.

as_dict() → dict

Serializes the AvgFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the AvgFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → AvgFunction

Deserializes the AvgFunction from a dictionary.

class databricks.sdk.service.ml.BackfillSource(delta_table_name: 'Optional[str]' = None, delta_table_source: 'Optional[DeltaTableSource]' = None)

delta_table_name: str | None = None

The full three-part name (catalog, schema, name) of the Delta table containing the historical data to backfill.

delta_table_source: DeltaTableSource | None = None

Deprecated: Use delta_table_name instead. Kept for backwards compatibility. The Delta table source containing the historical data to backfill. Only the delta table name is used for backfill, other fields are ignored.

as_dict() → dict

Serializes the BackfillSource into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the BackfillSource into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → BackfillSource

Deserializes the BackfillSource from a dictionary.

class databricks.sdk.service.ml.BatchCreateMaterializedFeaturesResponse(materialized_features: 'Optional[List[MaterializedFeature]]' = None)

materialized_features: List[MaterializedFeature] | None = None

The created materialized features with assigned IDs.

as_dict() → dict

Serializes the BatchCreateMaterializedFeaturesResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the BatchCreateMaterializedFeaturesResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → BatchCreateMaterializedFeaturesResponse

Deserializes the BatchCreateMaterializedFeaturesResponse from a dictionary.

class databricks.sdk.service.ml.ColumnIdentifier(variant_expr_path: 'str')

variant_expr_path: str

String representation of the column name using dot-prefixed path notation. For nested fields, the leaf value is what will be present in materialized tables and expected to match at query time. For example, the leaf node of value.trip_details.location_details.pickup_zip is pickup_zip.

as_dict() → dict

Serializes the ColumnIdentifier into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ColumnIdentifier into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ColumnIdentifier

Deserializes the ColumnIdentifier from a dictionary.

class databricks.sdk.service.ml.ColumnSelection(column: str)

A ColumnSelection function, equivalent to the LAST() record of an entity over a lifetime ContinuousWindow

column: str

Column name from source to select as the feature value.

as_dict() → dict

Serializes the ColumnSelection into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ColumnSelection into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ColumnSelection

Deserializes the ColumnSelection from a dictionary.

class databricks.sdk.service.ml.CommentActivityAction

An action that a user (with sufficient permissions) could take on an activity or comment. For activities, valid values are: * APPROVE_TRANSITION_REQUEST: Approve a transition request * REJECT_TRANSITION_REQUEST: Reject a transition request * CANCEL_TRANSITION_REQUEST: Cancel (delete) a transition request For comments, valid values are: * EDIT_COMMENT: Edit the comment * DELETE_COMMENT: Delete the comment

APPROVE_TRANSITION_REQUEST = "APPROVE_TRANSITION_REQUEST"

CANCEL_TRANSITION_REQUEST = "CANCEL_TRANSITION_REQUEST"

DELETE_COMMENT = "DELETE_COMMENT"

EDIT_COMMENT = "EDIT_COMMENT"

REJECT_TRANSITION_REQUEST = "REJECT_TRANSITION_REQUEST"

class databricks.sdk.service.ml.CommentObject(available_actions: List[CommentActivityAction] | None = None, comment: str | None = None, creation_timestamp: int | None = None, id: str | None = None, last_updated_timestamp: int | None = None, user_id: str | None = None)

For activities, this contains the activity recorded for the action. For comments, this contains the comment details. For transition requests, this contains the transition request details.

available_actions: List[CommentActivityAction] | None = None

Array of actions on the activity allowed for the current viewer.

comment: str | None = None

User-provided comment associated with the activity, comment, or transition request.

creation_timestamp: int | None = None

Creation time of the object, as a Unix timestamp in milliseconds.

id: str | None = None

Unique identifier for the object.

last_updated_timestamp: int | None = None

Time of the object at last update, as a Unix timestamp in milliseconds.

user_id: str | None = None

The username of the user that created the object.

as_dict() → dict

Serializes the CommentObject into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CommentObject into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CommentObject

Deserializes the CommentObject from a dictionary.

class databricks.sdk.service.ml.ContinuousWindow(window_duration: str, offset: str | None = None)

Deprecated: use RollingWindow with delay instead.

window_duration: str

The duration of the continuous window (must be positive).

offset: str | None = None

The offset of the continuous window (must be non-positive).

as_dict() → dict

Serializes the ContinuousWindow into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ContinuousWindow into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ContinuousWindow

Deserializes the ContinuousWindow from a dictionary.

class databricks.sdk.service.ml.CountFunction(input: str)

Computes the count of values.

input: str

The input column from which the count is computed. For Kafka sources, use dot-prefixed path notation (e.g., “value.amount”). For nested fields, the leaf node name is used. TODO(FS-939): Colon-prefixed notation (e.g., “value:amount”) is supported for backwards compatibility but is deprecated; migrate to dot notation.

as_dict() → dict

Serializes the CountFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CountFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CountFunction

Deserializes the CountFunction from a dictionary.

class databricks.sdk.service.ml.CreateCommentResponse(comment: 'Optional[CommentObject]' = None)

comment: CommentObject | None = None

New comment object

as_dict() → dict

Serializes the CreateCommentResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CreateCommentResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CreateCommentResponse

Deserializes the CreateCommentResponse from a dictionary.

class databricks.sdk.service.ml.CreateExperimentResponse(experiment_id: 'Optional[str]' = None)

experiment_id: str | None = None

Unique identifier for the experiment.

as_dict() → dict

Serializes the CreateExperimentResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CreateExperimentResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CreateExperimentResponse

Deserializes the CreateExperimentResponse from a dictionary.

class databricks.sdk.service.ml.CreateForecastingExperimentResponse(experiment_id: 'Optional[str]' = None)

experiment_id: str | None = None

The unique ID of the created forecasting experiment

as_dict() → dict

Serializes the CreateForecastingExperimentResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CreateForecastingExperimentResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CreateForecastingExperimentResponse

Deserializes the CreateForecastingExperimentResponse from a dictionary.

class databricks.sdk.service.ml.CreateLoggedModelResponse(model: 'Optional[LoggedModel]' = None)

model: LoggedModel | None = None

The newly created logged model.

as_dict() → dict

Serializes the CreateLoggedModelResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CreateLoggedModelResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CreateLoggedModelResponse

Deserializes the CreateLoggedModelResponse from a dictionary.

class databricks.sdk.service.ml.CreateMaterializedFeatureRequest(materialized_feature: 'MaterializedFeature')

materialized_feature: MaterializedFeature

The materialized feature to create.

as_dict() → dict

Serializes the CreateMaterializedFeatureRequest into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CreateMaterializedFeatureRequest into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CreateMaterializedFeatureRequest

Deserializes the CreateMaterializedFeatureRequest from a dictionary.

class databricks.sdk.service.ml.CreateModelResponse(registered_model: 'Optional[Model]' = None)

registered_model: Model | None = None

as_dict() → dict

Serializes the CreateModelResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CreateModelResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CreateModelResponse

Deserializes the CreateModelResponse from a dictionary.

class databricks.sdk.service.ml.CreateModelVersionResponse(model_version: 'Optional[ModelVersion]' = None)

model_version: ModelVersion | None = None

Return new version number generated for this model in registry.

as_dict() → dict

Serializes the CreateModelVersionResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CreateModelVersionResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CreateModelVersionResponse

Deserializes the CreateModelVersionResponse from a dictionary.

class databricks.sdk.service.ml.CreateRunResponse(run: 'Optional[Run]' = None)

run: Run | None = None

The newly created run.

as_dict() → dict

Serializes the CreateRunResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CreateRunResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CreateRunResponse

Deserializes the CreateRunResponse from a dictionary.

class databricks.sdk.service.ml.CreateTransitionRequestResponse(request: 'Optional[TransitionRequest]' = None)

request: TransitionRequest | None = None

New activity generated for stage transition request.

as_dict() → dict

Serializes the CreateTransitionRequestResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CreateTransitionRequestResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CreateTransitionRequestResponse

Deserializes the CreateTransitionRequestResponse from a dictionary.

class databricks.sdk.service.ml.CreateWebhookResponse(webhook: 'Optional[RegistryWebhook]' = None)

webhook: RegistryWebhook | None = None

as_dict() → dict

Serializes the CreateWebhookResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CreateWebhookResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CreateWebhookResponse

Deserializes the CreateWebhookResponse from a dictionary.

class databricks.sdk.service.ml.CronSchedule(cron_expression: str | None = None)

A cron-based schedule trigger for the materialization pipeline.

cron_expression: str | None = None

The cron expression defining the schedule (e.g., “0 0 * * *” for daily at midnight).

as_dict() → dict

Serializes the CronSchedule into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the CronSchedule into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → CronSchedule

Deserializes the CronSchedule from a dictionary.

class databricks.sdk.service.ml.DataSource(delta_table_source: DeltaTableSource | None = None, kafka_source: KafkaSource | None = None, request_source: RequestSource | None = None)

Specifies the data source backing a feature. Exactly one source type must be set.

delta_table_source: DeltaTableSource | None = None

A Delta table data source.

kafka_source: KafkaSource | None = None

A Kafka stream data source.

request_source: RequestSource | None = None

A request-time data source.

as_dict() → dict

Serializes the DataSource into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DataSource into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DataSource

Deserializes the DataSource from a dictionary.

class databricks.sdk.service.ml.Dataset(name: str, digest: str, source_type: str, source: str, profile: str | None = None, schema: str | None = None)

Dataset. Represents a reference to data used for training, testing, or evaluation during the model development process.

name: str

The name of the dataset. E.g. “my.uc.table@2” “nyc-taxi-dataset”, “fantastic-elk-3”

digest: str

Dataset digest, e.g. an md5 hash of the dataset that uniquely identifies it within datasets of the same name.

source_type: str

The type of the dataset source, e.g. ‘databricks-uc-table’, ‘DBFS’, ‘S3’, …

source: str

Source information for the dataset. Note that the source may not exactly reproduce the dataset if it was transformed / modified before use with MLflow.

profile: str | None = None

The profile of the dataset. Summary statistics for the dataset, such as the number of rows in a table, the mean / std / mode of each column in a table, or the number of elements in an array.

schema: str | None = None

The schema of the dataset. E.g., MLflow ColSpec JSON for a dataframe, MLflow TensorSpec JSON for an ndarray, or another schema format.

as_dict() → dict

Serializes the Dataset into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Dataset into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Dataset

Deserializes the Dataset from a dictionary.

class databricks.sdk.service.ml.DatasetInput(dataset: Dataset, tags: List[InputTag] | None = None)

DatasetInput. Represents a dataset and input tags.

dataset: Dataset

The dataset being used as a Run input.

tags: List[InputTag] | None = None

A list of tags for the dataset input, e.g. a “context” tag with value “training”

as_dict() → dict

Serializes the DatasetInput into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DatasetInput into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DatasetInput

Deserializes the DatasetInput from a dictionary.

class databricks.sdk.service.ml.DeleteCommentResponse

as_dict() → dict

Serializes the DeleteCommentResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteCommentResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteCommentResponse

Deserializes the DeleteCommentResponse from a dictionary.

class databricks.sdk.service.ml.DeleteExperimentResponse

as_dict() → dict

Serializes the DeleteExperimentResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteExperimentResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteExperimentResponse

Deserializes the DeleteExperimentResponse from a dictionary.

class databricks.sdk.service.ml.DeleteLoggedModelResponse

as_dict() → dict

Serializes the DeleteLoggedModelResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteLoggedModelResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteLoggedModelResponse

Deserializes the DeleteLoggedModelResponse from a dictionary.

class databricks.sdk.service.ml.DeleteLoggedModelTagResponse

as_dict() → dict

Serializes the DeleteLoggedModelTagResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteLoggedModelTagResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteLoggedModelTagResponse

Deserializes the DeleteLoggedModelTagResponse from a dictionary.

class databricks.sdk.service.ml.DeleteModelResponse

as_dict() → dict

Serializes the DeleteModelResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteModelResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteModelResponse

Deserializes the DeleteModelResponse from a dictionary.

class databricks.sdk.service.ml.DeleteModelTagResponse

as_dict() → dict

Serializes the DeleteModelTagResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteModelTagResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteModelTagResponse

Deserializes the DeleteModelTagResponse from a dictionary.

class databricks.sdk.service.ml.DeleteModelVersionResponse

as_dict() → dict

Serializes the DeleteModelVersionResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteModelVersionResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteModelVersionResponse

Deserializes the DeleteModelVersionResponse from a dictionary.

class databricks.sdk.service.ml.DeleteModelVersionTagResponse

as_dict() → dict

Serializes the DeleteModelVersionTagResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteModelVersionTagResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteModelVersionTagResponse

Deserializes the DeleteModelVersionTagResponse from a dictionary.

class databricks.sdk.service.ml.DeleteRunResponse

as_dict() → dict

Serializes the DeleteRunResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteRunResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteRunResponse

Deserializes the DeleteRunResponse from a dictionary.

class databricks.sdk.service.ml.DeleteRunsResponse(runs_deleted: 'Optional[int]' = None)

runs_deleted: int | None = None

The number of runs deleted.

as_dict() → dict

Serializes the DeleteRunsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteRunsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteRunsResponse

Deserializes the DeleteRunsResponse from a dictionary.

class databricks.sdk.service.ml.DeleteTagResponse

as_dict() → dict

Serializes the DeleteTagResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteTagResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteTagResponse

Deserializes the DeleteTagResponse from a dictionary.

class databricks.sdk.service.ml.DeleteTransitionRequestResponse(activity: 'Optional[Activity]' = None)

activity: Activity | None = None

New activity generated as a result of this operation.

as_dict() → dict

Serializes the DeleteTransitionRequestResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteTransitionRequestResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteTransitionRequestResponse

Deserializes the DeleteTransitionRequestResponse from a dictionary.

class databricks.sdk.service.ml.DeleteWebhookResponse

as_dict() → dict

Serializes the DeleteWebhookResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeleteWebhookResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeleteWebhookResponse

Deserializes the DeleteWebhookResponse from a dictionary.

class databricks.sdk.service.ml.DeltaTableSource(full_name: 'str', dataframe_schema: 'Optional[str]' = None, entity_columns: 'Optional[List[str]]' = None, filter_condition: 'Optional[str]' = None, timeseries_column: 'Optional[str]' = None, transformation_sql: 'Optional[str]' = None)

full_name: str

The full three-part (catalog, schema, table) name of the Delta table.

dataframe_schema: str | None = None

Schema of the resulting dataframe after transformations, in Spark StructType JSON format (from df.schema.json()). Required if transformation_sql is specified. Example: {“type”:”struct”,”fields”:[{“name”:”col_a”,”type”:”integer”,”nullable”:true,”metadata”:{}},{“name”:”col_c”,”type”:”integer”,”nullable”:true,”metadata”:{}}]}

entity_columns: List[str] | None = None

Deprecated: Use Feature.entity instead. Kept for backwards compatibility. The entity columns of the Delta table.

filter_condition: str | None = None

Single WHERE clause to filter delta table before applying transformations. Will be row-wise evaluated, so should only include conditionals and projections.

timeseries_column: str | None = None

Deprecated: Use Feature.timeseries_column instead. Kept for backwards compatibility. The timeseries column of the Delta table.

transformation_sql: str | None = None

A single SQL SELECT expression applied after filter_condition. Should contains all the columns needed (eg. “SELECT , col_a + col_b AS col_c FROM x.y.z WHERE col_a > 0” would have `transformation_sql` “, col_a + col_b AS col_c”) If transformation_sql is not provided, all columns of the delta table are present in the DataSource dataframe.

as_dict() → dict

Serializes the DeltaTableSource into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DeltaTableSource into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DeltaTableSource

Deserializes the DeltaTableSource from a dictionary.

class databricks.sdk.service.ml.DirectMtlsConfig(bootstrap_servers: str, mtls_config: MtlsConfig)

Direct connection configs for mTLS, as Kafka Connections do not support mTLS yet (XTA-18030). Temporarily used until UC Kafka Connections gain mTLS support.

bootstrap_servers: str

A comma-separated list of host:port pairs for the Kafka bootstrap servers.

mtls_config: MtlsConfig

Mutual-TLS authentication configuration.

as_dict() → dict

Serializes the DirectMtlsConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DirectMtlsConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DirectMtlsConfig

Deserializes the DirectMtlsConfig from a dictionary.

class databricks.sdk.service.ml.DirectSchemas(key_schema: SchemaConfig | None = None, payload_schema: SchemaConfig | None = None)

Schema definitions provided directly on the Stream, as opposed to referencing a schema registry. In a future milestone, we will support schema registries through a UC Connection.

key_schema: SchemaConfig | None = None

Schema for the message key. This is only used for Kafka streams. For Kafka, at least one of payload_schema or key_schema must be specified.

payload_schema: SchemaConfig | None = None

Schema for the message payload. For Kafka, this is the value schema. Unless the platform supports another schema (e.g. keys for Kafka), this must be specified.

as_dict() → dict

Serializes the DirectSchemas into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the DirectSchemas into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → DirectSchemas

Deserializes the DirectSchemas from a dictionary.

class databricks.sdk.service.ml.EntityColumn(name: 'str')

name: str

The name of the entity column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., “value.user_id”, “key.partition_key”). For nested fields, the leaf node name (e.g., “user_id” from “value.trip_details.user_id”) is what will be present in materialized tables and expected to match at query time. TODO(FS-939): Colon-prefixed notation (e.g., “value:user_id”) is supported for backwards compatibility but is deprecated; migrate to dot notation.

as_dict() → dict

Serializes the EntityColumn into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the EntityColumn into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → EntityColumn

Deserializes the EntityColumn from a dictionary.

class databricks.sdk.service.ml.Experiment(artifact_location: str | None = None, creation_time: int | None = None, experiment_id: str | None = None, last_update_time: int | None = None, lifecycle_stage: str | None = None, name: str | None = None, tags: List[ExperimentTag] | None = None)

An experiment and its metadata.

artifact_location: str | None = None

Location where artifacts for the experiment are stored.

creation_time: int | None = None

Creation time

experiment_id: str | None = None

Unique identifier for the experiment.

last_update_time: int | None = None

Last update time

lifecycle_stage: str | None = None

Current life cycle stage of the experiment: “active” or “deleted”. Deleted experiments are not returned by APIs.

name: str | None = None

Human readable name that identifies the experiment.

tags: List[ExperimentTag] | None = None

Tags: Additional metadata key-value pairs.

as_dict() → dict

Serializes the Experiment into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Experiment into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Experiment

Deserializes the Experiment from a dictionary.

class databricks.sdk.service.ml.ExperimentAccessControlRequest(group_name: 'Optional[str]' = None, permission_level: 'Optional[ExperimentPermissionLevel]' = None, service_principal_name: 'Optional[str]' = None, user_name: 'Optional[str]' = None)

group_name: str | None = None

name of the group

permission_level: ExperimentPermissionLevel | None = None

service_principal_name: str | None = None

application ID of a service principal

user_name: str | None = None

name of the user

as_dict() → dict

Serializes the ExperimentAccessControlRequest into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ExperimentAccessControlRequest into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ExperimentAccessControlRequest

Deserializes the ExperimentAccessControlRequest from a dictionary.

class databricks.sdk.service.ml.ExperimentAccessControlResponse(all_permissions: 'Optional[List[ExperimentPermission]]' = None, display_name: 'Optional[str]' = None, group_name: 'Optional[str]' = None, service_principal_name: 'Optional[str]' = None, user_name: 'Optional[str]' = None)

all_permissions: List[ExperimentPermission] | None = None

All permissions.

display_name: str | None = None

Display name of the user or service principal.

group_name: str | None = None

name of the group

service_principal_name: str | None = None

Name of the service principal.

user_name: str | None = None

name of the user

as_dict() → dict

Serializes the ExperimentAccessControlResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ExperimentAccessControlResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ExperimentAccessControlResponse

Deserializes the ExperimentAccessControlResponse from a dictionary.

class databricks.sdk.service.ml.ExperimentPermission(inherited: 'Optional[bool]' = None, inherited_from_object: 'Optional[List[str]]' = None, permission_level: 'Optional[ExperimentPermissionLevel]' = None)

inherited: bool | None = None

inherited_from_object: List[str] | None = None

permission_level: ExperimentPermissionLevel | None = None

as_dict() → dict

Serializes the ExperimentPermission into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ExperimentPermission into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ExperimentPermission

Deserializes the ExperimentPermission from a dictionary.

class databricks.sdk.service.ml.ExperimentPermissionLevel

Permission level

CAN_EDIT = "CAN_EDIT"

CAN_MANAGE = "CAN_MANAGE"

CAN_READ = "CAN_READ"

class databricks.sdk.service.ml.ExperimentPermissions(access_control_list: 'Optional[List[ExperimentAccessControlResponse]]' = None, object_id: 'Optional[str]' = None, object_type: 'Optional[str]' = None)

access_control_list: List[ExperimentAccessControlResponse] | None = None

object_id: str | None = None

object_type: str | None = None

as_dict() → dict

Serializes the ExperimentPermissions into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ExperimentPermissions into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ExperimentPermissions

Deserializes the ExperimentPermissions from a dictionary.

class databricks.sdk.service.ml.ExperimentPermissionsDescription(description: 'Optional[str]' = None, permission_level: 'Optional[ExperimentPermissionLevel]' = None)

description: str | None = None

permission_level: ExperimentPermissionLevel | None = None

as_dict() → dict

Serializes the ExperimentPermissionsDescription into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ExperimentPermissionsDescription into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ExperimentPermissionsDescription

Deserializes the ExperimentPermissionsDescription from a dictionary.

class databricks.sdk.service.ml.ExperimentTag(key: str | None = None, value: str | None = None)

A tag for an experiment.

key: str | None = None

The tag key.

value: str | None = None

The tag value.

as_dict() → dict

Serializes the ExperimentTag into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ExperimentTag into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ExperimentTag

Deserializes the ExperimentTag from a dictionary.

class databricks.sdk.service.ml.Feature(full_name: 'str', source: 'DataSource', function: 'Function', catalog_name: 'Optional[str]' = None, created_at: 'Optional[Timestamp]' = None, created_by: 'Optional[str]' = None, description: 'Optional[str]' = None, entities: 'Optional[List[EntityColumn]]' = None, filter_condition: 'Optional[str]' = None, inputs: 'Optional[List[str]]' = None, lineage_context: 'Optional[LineageContext]' = None, name: 'Optional[str]' = None, schema_name: 'Optional[str]' = None, time_window: 'Optional[TimeWindow]' = None, timeseries_column: 'Optional[TimeseriesColumn]' = None)

full_name: str

The full three-part name (catalog, schema, name) of the feature. This is the feature’s resource identifier; the catalog_name, schema_name, and name fields below are OUTPUT_ONLY decomposed views of this value.

source: DataSource

The data source of the feature.

function: Function

The function by which the feature is computed.

catalog_name: str | None = None

Name of parent catalog.

created_at: Timestamp | None = None

Time at which this feature was created.

created_by: str | None = None

Username of the feature creator.

description: str | None = None

The description of the feature.

entities: List[EntityColumn] | None = None

The entity columns for the feature, used as aggregation keys and for query-time lookup.

filter_condition: str | None = None

Deprecated: Use DeltaTableSource.filter_condition or KafkaSource.filter_condition instead. Kept for backwards compatibility. The filter condition applied to the source data before aggregation.

inputs: List[str] | None = None

Deprecated: Use AggregationFunction.inputs instead. Kept for backwards compatibility. The input columns from which the feature is computed.

lineage_context: LineageContext | None = None

Lineage context information for this feature. WARNING: This field is primarily intended for internal use by Databricks systems and is automatically populated when features are created through Databricks notebooks or jobs. Users should not manually set this field as incorrect values may lead to inaccurate lineage tracking or unexpected behavior. This field will be set by feature-engineering client and should be left unset by SDK and terraform users.

name: str | None = None

Name of the feature, extracted from the full three-part name (catalog.schema.name).

schema_name: str | None = None

Name of parent schema relative to its parent catalog.

time_window: TimeWindow | None = None

Deprecated: Use Function.aggregation_function.time_window instead. Kept for backwards compatibility. The time window in which the feature is computed.

timeseries_column: TimeseriesColumn | None = None

Column recording time, used for point-in-time joins, backfills, and aggregations.

as_dict() → dict

Serializes the Feature into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Feature into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Feature

Deserializes the Feature from a dictionary.

class databricks.sdk.service.ml.FeatureLineage(feature_specs: 'Optional[List[FeatureLineageFeatureSpec]]' = None, models: 'Optional[List[FeatureLineageModel]]' = None, online_features: 'Optional[List[FeatureLineageOnlineFeature]]' = None)

feature_specs: List[FeatureLineageFeatureSpec] | None = None

List of feature specs that contain this feature.

models: List[FeatureLineageModel] | None = None

List of Unity Catalog models that were trained on this feature.

online_features: List[FeatureLineageOnlineFeature] | None = None

List of online features that use this feature as source.

as_dict() → dict

Serializes the FeatureLineage into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FeatureLineage into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FeatureLineage

Deserializes the FeatureLineage from a dictionary.

class databricks.sdk.service.ml.FeatureLineageFeatureSpec(name: 'Optional[str]' = None)

name: str | None = None

The full name of the feature spec in Unity Catalog.

as_dict() → dict

Serializes the FeatureLineageFeatureSpec into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FeatureLineageFeatureSpec into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FeatureLineageFeatureSpec

Deserializes the FeatureLineageFeatureSpec from a dictionary.

class databricks.sdk.service.ml.FeatureLineageModel(name: 'Optional[str]' = None, version: 'Optional[int]' = None)

name: str | None = None

The full name of the model in Unity Catalog.

version: int | None = None

The version of the model.

as_dict() → dict

Serializes the FeatureLineageModel into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FeatureLineageModel into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FeatureLineageModel

Deserializes the FeatureLineageModel from a dictionary.

class databricks.sdk.service.ml.FeatureLineageOnlineFeature(feature_name: 'Optional[str]' = None, table_name: 'Optional[str]' = None)

feature_name: str | None = None

The name of the online feature (column name).

table_name: str | None = None

The full name of the online table in Unity Catalog.

as_dict() → dict

Serializes the FeatureLineageOnlineFeature into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FeatureLineageOnlineFeature into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FeatureLineageOnlineFeature

Deserializes the FeatureLineageOnlineFeature from a dictionary.

class databricks.sdk.service.ml.FeatureList(features: List[LinkedFeature] | None = None)

Feature list wrap all the features for a model version

features: List[LinkedFeature] | None = None

as_dict() → dict

Serializes the FeatureList into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FeatureList into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FeatureList

Deserializes the FeatureList from a dictionary.

class databricks.sdk.service.ml.FeatureTag(key: str, value: str | None = None)

Represents a tag on a feature in a feature table.

key: str

value: str | None = None

as_dict() → dict

Serializes the FeatureTag into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FeatureTag into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FeatureTag

Deserializes the FeatureTag from a dictionary.

class databricks.sdk.service.ml.FieldDefinition(name: str, data_type: ScalarDataType)

A single field definition within a FlatSchema, specifying the field name and its scalar data type. Does not support nested or complex types (arrays, maps, structs).

name: str

The name of the field.

data_type: ScalarDataType

The scalar data type of the field.

as_dict() → dict

Serializes the FieldDefinition into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FieldDefinition into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FieldDefinition

Deserializes the FieldDefinition from a dictionary.

class databricks.sdk.service.ml.FileInfo(file_size: int | None = None, is_dir: bool | None = None, path: str | None = None)

Metadata of a single artifact file or directory.

file_size: int | None = None

The size in bytes of the file. Unset for directories.

is_dir: bool | None = None

Whether the path is a directory.

path: str | None = None

The path relative to the root artifact directory run.

as_dict() → dict

Serializes the FileInfo into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FileInfo into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FileInfo

Deserializes the FileInfo from a dictionary.

class databricks.sdk.service.ml.FinalizeLoggedModelResponse(model: 'Optional[LoggedModel]' = None)

model: LoggedModel | None = None

The updated logged model.

as_dict() → dict

Serializes the FinalizeLoggedModelResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FinalizeLoggedModelResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FinalizeLoggedModelResponse

Deserializes the FinalizeLoggedModelResponse from a dictionary.

class databricks.sdk.service.ml.FirstFunction(input: str)

Returns the first value.

input: str

The input column from which the first value is returned.

as_dict() → dict

Serializes the FirstFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FirstFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FirstFunction

Deserializes the FirstFunction from a dictionary.

class databricks.sdk.service.ml.FlatSchema(fields: List[FieldDefinition])

A flat (non-nested) schema for request-time fields, defined as an ordered list of field definitions. This schema only supports scalar types.

fields: List[FieldDefinition]

The list of fields in this schema.

as_dict() → dict

Serializes the FlatSchema into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FlatSchema into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FlatSchema

Deserializes the FlatSchema from a dictionary.

class databricks.sdk.service.ml.ForecastingExperiment(experiment_id: str | None = None, experiment_page_url: str | None = None, state: ForecastingExperimentState | None = None)

Represents a forecasting experiment with its unique identifier, URL, and state.

experiment_id: str | None = None

The unique ID for the forecasting experiment.

experiment_page_url: str | None = None

The URL to the forecasting experiment page.

state: ForecastingExperimentState | None = None

The current state of the forecasting experiment.

as_dict() → dict

Serializes the ForecastingExperiment into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ForecastingExperiment into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ForecastingExperiment

Deserializes the ForecastingExperiment from a dictionary.

class databricks.sdk.service.ml.ForecastingExperimentState

CANCELLED = "CANCELLED"

FAILED = "FAILED"

PENDING = "PENDING"

RUNNING = "RUNNING"

SUCCEEDED = "SUCCEEDED"

class databricks.sdk.service.ml.Function(aggregation_function: 'Optional[AggregationFunction]' = None, column_selection: 'Optional[ColumnSelection]' = None, extra_parameters: 'Optional[List[FunctionExtraParameter]]' = None, function_type: 'Optional[FunctionFunctionType]' = None)

aggregation_function: AggregationFunction | None = None

An aggregation function applied over a time window.

column_selection: ColumnSelection | None = None

Selects the latest value of a single column in a data source

extra_parameters: List[FunctionExtraParameter] | None = None

Deprecated: Use the function oneof with AggregationFunction instead. Kept for backwards compatibility. Extra parameters for parameterized functions.

function_type: FunctionFunctionType | None = None

Deprecated: Use the function oneof with AggregationFunction instead. Kept for backwards compatibility. The type of the function.

as_dict() → dict

Serializes the Function into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Function into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Function

Deserializes the Function from a dictionary.

class databricks.sdk.service.ml.FunctionExtraParameter(key: str, value: str)

Deprecated: Use typed fields on function-specific messages (e.g. ApproxPercentileFunction.percentile) or AggregationFunction.ExtraParameter instead. Kept for backwards compatibility.

key: str

The name of the parameter.

value: str

The value of the parameter.

as_dict() → dict

Serializes the FunctionExtraParameter into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the FunctionExtraParameter into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → FunctionExtraParameter

Deserializes the FunctionExtraParameter from a dictionary.

class databricks.sdk.service.ml.FunctionFunctionType

Deprecated: Use the function-specific messages in AggregationFunction.function_type oneof instead. Kept for backwards compatibility.

APPROX_COUNT_DISTINCT = "APPROX_COUNT_DISTINCT"

APPROX_PERCENTILE = "APPROX_PERCENTILE"

AVG = "AVG"

COUNT = "COUNT"

FIRST = "FIRST"

LAST = "LAST"

MAX = "MAX"

MIN = "MIN"

STDDEV_POP = "STDDEV_POP"

STDDEV_SAMP = "STDDEV_SAMP"

SUM = "SUM"

VAR_POP = "VAR_POP"

VAR_SAMP = "VAR_SAMP"

class databricks.sdk.service.ml.GetExperimentByNameResponse(experiment: 'Optional[Experiment]' = None)

experiment: Experiment | None = None

Experiment details.

as_dict() → dict

Serializes the GetExperimentByNameResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetExperimentByNameResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetExperimentByNameResponse

Deserializes the GetExperimentByNameResponse from a dictionary.

class databricks.sdk.service.ml.GetExperimentPermissionLevelsResponse(permission_levels: 'Optional[List[ExperimentPermissionsDescription]]' = None)

permission_levels: List[ExperimentPermissionsDescription] | None = None

Specific permission levels

as_dict() → dict

Serializes the GetExperimentPermissionLevelsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetExperimentPermissionLevelsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetExperimentPermissionLevelsResponse

Deserializes the GetExperimentPermissionLevelsResponse from a dictionary.

class databricks.sdk.service.ml.GetExperimentResponse(experiment: 'Optional[Experiment]' = None)

experiment: Experiment | None = None

Experiment details.

as_dict() → dict

Serializes the GetExperimentResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetExperimentResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetExperimentResponse

Deserializes the GetExperimentResponse from a dictionary.

class databricks.sdk.service.ml.GetLatestVersionsResponse(model_versions: 'Optional[List[ModelVersion]]' = None)

model_versions: List[ModelVersion] | None = None

Latest version models for each requests stage. Only return models with current READY status. If no stages provided, returns the latest version for each stage, including “None”.

as_dict() → dict

Serializes the GetLatestVersionsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetLatestVersionsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetLatestVersionsResponse

Deserializes the GetLatestVersionsResponse from a dictionary.

class databricks.sdk.service.ml.GetLoggedModelResponse(model: 'Optional[LoggedModel]' = None)

model: LoggedModel | None = None

The retrieved logged model.

as_dict() → dict

Serializes the GetLoggedModelResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetLoggedModelResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetLoggedModelResponse

Deserializes the GetLoggedModelResponse from a dictionary.

class databricks.sdk.service.ml.GetMetricHistoryResponse(metrics: 'Optional[List[Metric]]' = None, next_page_token: 'Optional[str]' = None)

metrics: List[Metric] | None = None

All logged values for this metric if max_results is not specified in the request or if the total count of metrics returned is less than the service level pagination threshold. Otherwise, this is one page of results.

next_page_token: str | None = None

A token that can be used to issue a query for the next page of metric history values. A missing token indicates that no additional metrics are available to fetch.

as_dict() → dict

Serializes the GetMetricHistoryResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetMetricHistoryResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetMetricHistoryResponse

Deserializes the GetMetricHistoryResponse from a dictionary.

class databricks.sdk.service.ml.GetModelResponse(registered_model_databricks: 'Optional[ModelDatabricks]' = None)

registered_model_databricks: ModelDatabricks | None = None

as_dict() → dict

Serializes the GetModelResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetModelResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetModelResponse

Deserializes the GetModelResponse from a dictionary.

class databricks.sdk.service.ml.GetModelVersionDownloadUriResponse(artifact_uri: 'Optional[str]' = None)

artifact_uri: str | None = None

URI corresponding to where artifacts for this model version are stored.

as_dict() → dict

Serializes the GetModelVersionDownloadUriResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetModelVersionDownloadUriResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetModelVersionDownloadUriResponse

Deserializes the GetModelVersionDownloadUriResponse from a dictionary.

class databricks.sdk.service.ml.GetModelVersionResponse(model_version: 'Optional[ModelVersion]' = None)

model_version: ModelVersion | None = None

as_dict() → dict

Serializes the GetModelVersionResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetModelVersionResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetModelVersionResponse

Deserializes the GetModelVersionResponse from a dictionary.

class databricks.sdk.service.ml.GetRegisteredModelPermissionLevelsResponse(permission_levels: 'Optional[List[RegisteredModelPermissionsDescription]]' = None)

permission_levels: List[RegisteredModelPermissionsDescription] | None = None

Specific permission levels

as_dict() → dict

Serializes the GetRegisteredModelPermissionLevelsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetRegisteredModelPermissionLevelsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetRegisteredModelPermissionLevelsResponse

Deserializes the GetRegisteredModelPermissionLevelsResponse from a dictionary.

class databricks.sdk.service.ml.GetRunResponse(run: 'Optional[Run]' = None)

run: Run | None = None

Run metadata (name, start time, etc) and data (metrics, params, and tags).

as_dict() → dict

Serializes the GetRunResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the GetRunResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → GetRunResponse

Deserializes the GetRunResponse from a dictionary.

class databricks.sdk.service.ml.HttpUrlSpec(url: 'str', authorization: 'Optional[str]' = None, enable_ssl_verification: 'Optional[bool]' = None, secret: 'Optional[str]' = None)

url: str

External HTTPS URL called on event trigger (by using a POST request).

authorization: str | None = None

Value of the authorization header that should be sent in the request sent by the wehbook. It should be of the form “<auth type> <credentials>”. If set to an empty string, no authorization header will be included in the request.

enable_ssl_verification: bool | None = None

Enable/disable SSL certificate validation. Default is true. For self-signed certificates, this field must be false AND the destination server must disable certificate validation as well. For security purposes, it is encouraged to perform secret validation with the HMAC-encoded portion of the payload and acknowledge the risk associated with disabling hostname validation whereby it becomes more likely that requests can be maliciously routed to an unintended host.

secret: str | None = None

Shared secret required for HMAC encoding payload. The HMAC-encoded payload will be sent in the header as: { “X-Databricks-Signature”: $encoded_payload }.

as_dict() → dict

Serializes the HttpUrlSpec into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the HttpUrlSpec into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → HttpUrlSpec

Deserializes the HttpUrlSpec from a dictionary.

class databricks.sdk.service.ml.HttpUrlSpecWithoutSecret(enable_ssl_verification: 'Optional[bool]' = None, url: 'Optional[str]' = None)

enable_ssl_verification: bool | None = None

Enable/disable SSL certificate validation. Default is true. For self-signed certificates, this field must be false AND the destination server must disable certificate validation as well. For security purposes, it is encouraged to perform secret validation with the HMAC-encoded portion of the payload and acknowledge the risk associated with disabling hostname validation whereby it becomes more likely that requests can be maliciously routed to an unintended host.

url: str | None = None

External HTTPS URL called on event trigger (by using a POST request).

as_dict() → dict

Serializes the HttpUrlSpecWithoutSecret into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the HttpUrlSpecWithoutSecret into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → HttpUrlSpecWithoutSecret

Deserializes the HttpUrlSpecWithoutSecret from a dictionary.

class databricks.sdk.service.ml.IngestionConfig(ingestion_destination: IngestionDestination, backfill_job_id: int | None = None, backfill_source: BackfillSource | None = None, deduplication_columns: List[str] | None = None, ingestion_job_id: int | None = None, ingestion_pipeline_id: str | None = None)

Configuration for the Databricks-managed ingestion pipeline. Groups the ingestion destination (required) and optional backfill source.

ingestion_destination: IngestionDestination

Destination for the Databricks-managed Delta table that holds an offline copy of the streaming data for querying and training. This table contains both 1) forward-filled data from the Stream and 2) backfilled data from the BackfillSource (if provided). This table is created and managed by Databricks and is deleted when the Stream is deleted.

backfill_job_id: int | None = None

The ID of the Databricks Job that performs the historical backfill of the ingestion Delta table.

backfill_source: BackfillSource | None = None

A user-provided source for backfilling data. Historical data is used when creating a training set from streaming features linked to this Stream. The backfill data stored in this location will be copied into the ingestion table for offline querying and training. The schema for this source must match exactly that of the key and payload schemas specified for this Stream.

deduplication_columns: List[str] | None = None

Column paths used to identify duplicate rows during ingestion; only one row per distinct combination of these values is kept. Use dot notation for nested fields (e.g. value.user_id). Empty list means every column is compared.

ingestion_job_id: int | None = None

The ID of the Databricks Job that performs the forward-fill ingestion.

ingestion_pipeline_id: str | None = None

The ID of the SDP pipeline that continuously copies new events from the streaming source into the ingestion Delta table.

as_dict() → dict

Serializes the IngestionConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the IngestionConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → IngestionConfig

Deserializes the IngestionConfig from a dictionary.

class databricks.sdk.service.ml.IngestionDestination(delta_table_name: str | None = None)

Destination for the Databricks-managed Delta table that holds an offline copy of the streaming data for querying and training.

delta_table_name: str | None = None

The full three-part name (catalog, schema, name) of the Delta table to be created for ingestion.

as_dict() → dict

Serializes the IngestionDestination into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the IngestionDestination into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → IngestionDestination

Deserializes the IngestionDestination from a dictionary.

class databricks.sdk.service.ml.InputTag(key: str, value: str)

Tag for a dataset input.

key: str

The tag key.

value: str

The tag value.

as_dict() → dict

Serializes the InputTag into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the InputTag into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → InputTag

Deserializes the InputTag from a dictionary.

class databricks.sdk.service.ml.JobContext(job_id: 'Optional[int]' = None, job_run_id: 'Optional[int]' = None)

job_id: int | None = None

The job ID where this API invoked.

job_run_id: int | None = None

The job run ID where this API was invoked.

as_dict() → dict

Serializes the JobContext into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the JobContext into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → JobContext

Deserializes the JobContext from a dictionary.

class databricks.sdk.service.ml.JobSpec(job_id: 'str', access_token: 'str', workspace_url: 'Optional[str]' = None)

job_id: str

ID of the job that the webhook runs.

access_token: str

The personal access token used to authorize webhook’s job runs.

workspace_url: str | None = None

URL of the workspace containing the job that this webhook runs. If not specified, the job’s workspace URL is assumed to be the same as the workspace where the webhook is created.

as_dict() → dict

Serializes the JobSpec into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the JobSpec into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → JobSpec

Deserializes the JobSpec from a dictionary.

class databricks.sdk.service.ml.JobSpecWithoutSecret(job_id: 'Optional[str]' = None, workspace_url: 'Optional[str]' = None)

job_id: str | None = None

ID of the job that the webhook runs.

workspace_url: str | None = None

URL of the workspace containing the job that this webhook runs. If not specified, the job’s workspace URL is assumed to be the same as the workspace where the webhook is created.

as_dict() → dict

Serializes the JobSpecWithoutSecret into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the JobSpecWithoutSecret into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → JobSpecWithoutSecret

Deserializes the JobSpecWithoutSecret from a dictionary.

class databricks.sdk.service.ml.KafkaConfig(name: 'str', bootstrap_servers: 'str', subscription_mode: 'SubscriptionMode', auth_config: 'AuthConfig', backfill_source: 'Optional[BackfillSource]' = None, extra_options: 'Optional[Dict[str, str]]' = None, key_schema: 'Optional[SchemaConfig]' = None, value_schema: 'Optional[SchemaConfig]' = None)

name: str

Name that uniquely identifies this Kafka config within the metastore. This will be the identifier used from the Feature object to reference these configs for a feature. Can be distinct from topic name.

bootstrap_servers: str

A comma-separated list of host/port pairs pointing to Kafka cluster.

subscription_mode: SubscriptionMode

Options to configure which Kafka topics to pull data from.

auth_config: AuthConfig

Authentication configuration for connection to topics.

backfill_source: BackfillSource | None = None

A user-provided and managed source for backfilling data. Historical data is used when creating a training set from streaming features linked to this Kafka config. In the future, a separate table will be maintained by Databricks for forward filling data. The schema for this source must match exactly that of the key and value schemas specified for this Kafka config.

extra_options: Dict[str, str] | None = None

Catch-all for miscellaneous options. Keys should be source options or Kafka consumer options (kafka.*)

key_schema: SchemaConfig | None = None

Schema configuration for extracting message keys from topics. At least one of key_schema and value_schema must be provided.

value_schema: SchemaConfig | None = None

Schema configuration for extracting message values from topics. At least one of key_schema and value_schema must be provided.

as_dict() → dict

Serializes the KafkaConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the KafkaConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → KafkaConfig

Deserializes the KafkaConfig from a dictionary.

class databricks.sdk.service.ml.KafkaSource(name: 'str', entity_column_identifiers: 'Optional[List[ColumnIdentifier]]' = None, filter_condition: 'Optional[str]' = None, timeseries_column_identifier: 'Optional[ColumnIdentifier]' = None)

name: str

Name of the Kafka source, used to identify it. This is used to look up the corresponding KafkaConfig object. Can be distinct from topic name.

entity_column_identifiers: List[ColumnIdentifier] | None = None

Deprecated: Use Feature.entity instead. Kept for backwards compatibility. The entity column identifiers of the Kafka source.

filter_condition: str | None = None

The filter condition applied to the source data before aggregation.

timeseries_column_identifier: ColumnIdentifier | None = None

Deprecated: Use Feature.timeseries_column instead. Kept for backwards compatibility. The timeseries column identifier of the Kafka source.

as_dict() → dict

Serializes the KafkaSource into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the KafkaSource into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → KafkaSource

Deserializes the KafkaSource from a dictionary.

class databricks.sdk.service.ml.KafkaStreamConfig(subscription_mode: KafkaSubscriptionMode, extra_options: Dict[str, str] | None = None)

Kafka-specific configuration for a Stream.

subscription_mode: KafkaSubscriptionMode

Options to configure which Kafka topics to pull data from.

extra_options: Dict[str, str] | None = None

Miscellaneous source options. Accepted keys are source options or Kafka consumer options (kafka.*), validated against an allow-list at request time. All auth configuration goes through the underlying UC Connection(s) or configs and should not be stored here.

as_dict() → dict

Serializes the KafkaStreamConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the KafkaStreamConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → KafkaStreamConfig

Deserializes the KafkaStreamConfig from a dictionary.

class databricks.sdk.service.ml.KafkaSubscriptionMode(assign: str | None = None, subscribe: str | None = None, subscribe_pattern: str | None = None)

Subscription mode for Kafka topic selection, matching standard Spark Structured Streaming options.

assign: str | None = None

A JSON string that contains the specific topic-partitions to consume from. For example, for ‘{“topicA”:[0,1],”topicB”:[2,4]}’, topicA’s 0’th and 1st partitions will be consumed from.

subscribe: str | None = None

A comma-separated list of Kafka topics to read from. For example, ‘topicA,topicB,topicC’.

subscribe_pattern: str | None = None

A regular expression matching topics to subscribe to. For example, ‘topic.*’ will subscribe to all topics starting with ‘topic’.

as_dict() → dict

Serializes the KafkaSubscriptionMode into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the KafkaSubscriptionMode into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → KafkaSubscriptionMode

Deserializes the KafkaSubscriptionMode from a dictionary.

class databricks.sdk.service.ml.LastFunction(input: str)

Returns the last value.

input: str

The input column from which the last value is returned.

as_dict() → dict

Serializes the LastFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LastFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LastFunction

Deserializes the LastFunction from a dictionary.

class databricks.sdk.service.ml.LineageContext(job_context: JobContext | None = None, notebook_id: int | None = None)

Lineage context information for tracking where an API was invoked. This will allow us to track lineage, which currently uses caller entity information for use across the Lineage Client and Observability in Lumberjack.

job_context: JobContext | None = None

Job context information including job ID and run ID.

notebook_id: int | None = None

The notebook ID where this API was invoked.

as_dict() → dict

Serializes the LineageContext into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LineageContext into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LineageContext

Deserializes the LineageContext from a dictionary.

class databricks.sdk.service.ml.LinkedFeature(feature_name: str | None = None, feature_table_id: str | None = None, feature_table_name: str | None = None)

Feature for model version. ([ML-57150] Renamed from Feature to LinkedFeature)

feature_name: str | None = None

Feature name

feature_table_id: str | None = None

Feature table id

feature_table_name: str | None = None

Feature table name

as_dict() → dict

Serializes the LinkedFeature into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LinkedFeature into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LinkedFeature

Deserializes the LinkedFeature from a dictionary.

class databricks.sdk.service.ml.ListArtifactsResponse(files: 'Optional[List[FileInfo]]' = None, next_page_token: 'Optional[str]' = None, root_uri: 'Optional[str]' = None)

files: List[FileInfo] | None = None

The file location and metadata for artifacts.

next_page_token: str | None = None

The token that can be used to retrieve the next page of artifact results.

root_uri: str | None = None

The root artifact directory for the run.

as_dict() → dict

Serializes the ListArtifactsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListArtifactsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListArtifactsResponse

Deserializes the ListArtifactsResponse from a dictionary.

class databricks.sdk.service.ml.ListExperimentsResponse(experiments: 'Optional[List[Experiment]]' = None, next_page_token: 'Optional[str]' = None)

experiments: List[Experiment] | None = None

Paginated Experiments beginning with the first item on the requested page.

next_page_token: str | None = None

Token that can be used to retrieve the next page of experiments. Empty token means no more experiment is available for retrieval.

as_dict() → dict

Serializes the ListExperimentsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListExperimentsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListExperimentsResponse

Deserializes the ListExperimentsResponse from a dictionary.

class databricks.sdk.service.ml.ListFeatureTagsResponse(feature_tags: List[FeatureTag] | None = None, next_page_token: str | None = None)

Response message for ListFeatureTag.

feature_tags: List[FeatureTag] | None = None

next_page_token: str | None = None

Pagination token to request the next page of results for this query.

as_dict() → dict

Serializes the ListFeatureTagsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListFeatureTagsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListFeatureTagsResponse

Deserializes the ListFeatureTagsResponse from a dictionary.

class databricks.sdk.service.ml.ListFeaturesResponse(features: 'Optional[List[Feature]]' = None, next_page_token: 'Optional[str]' = None)

features: List[Feature] | None = None

List of features.

next_page_token: str | None = None

Pagination token to request the next page of results for this query.

as_dict() → dict

Serializes the ListFeaturesResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListFeaturesResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListFeaturesResponse

Deserializes the ListFeaturesResponse from a dictionary.

class databricks.sdk.service.ml.ListKafkaConfigsResponse(kafka_configs: 'List[KafkaConfig]', next_page_token: 'Optional[str]' = None)

kafka_configs: List[KafkaConfig]

List of Kafka configs. Schemas are not included in the response.

next_page_token: str | None = None

Pagination token to request the next page of results for this query.

as_dict() → dict

Serializes the ListKafkaConfigsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListKafkaConfigsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListKafkaConfigsResponse

Deserializes the ListKafkaConfigsResponse from a dictionary.

class databricks.sdk.service.ml.ListMaterializedFeaturesResponse(materialized_features: 'Optional[List[MaterializedFeature]]' = None, next_page_token: 'Optional[str]' = None)

materialized_features: List[MaterializedFeature] | None = None

List of materialized features.

next_page_token: str | None = None

Pagination token to request the next page of results for this query.

as_dict() → dict

Serializes the ListMaterializedFeaturesResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListMaterializedFeaturesResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListMaterializedFeaturesResponse

Deserializes the ListMaterializedFeaturesResponse from a dictionary.

class databricks.sdk.service.ml.ListModelsResponse(next_page_token: 'Optional[str]' = None, registered_models: 'Optional[List[Model]]' = None)

next_page_token: str | None = None

Pagination token to request next page of models for the same query.

registered_models: List[Model] | None = None

as_dict() → dict

Serializes the ListModelsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListModelsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListModelsResponse

Deserializes the ListModelsResponse from a dictionary.

class databricks.sdk.service.ml.ListOnlineStoresResponse(next_page_token: 'Optional[str]' = None, online_stores: 'Optional[List[OnlineStore]]' = None)

next_page_token: str | None = None

Pagination token to request the next page of results for this query.

online_stores: List[OnlineStore] | None = None

List of online stores.

as_dict() → dict

Serializes the ListOnlineStoresResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListOnlineStoresResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListOnlineStoresResponse

Deserializes the ListOnlineStoresResponse from a dictionary.

class databricks.sdk.service.ml.ListRegistryWebhooks(next_page_token: 'Optional[str]' = None, webhooks: 'Optional[List[RegistryWebhook]]' = None)

next_page_token: str | None = None

Token that can be used to retrieve the next page of artifact results

webhooks: List[RegistryWebhook] | None = None

Array of registry webhooks.

as_dict() → dict

Serializes the ListRegistryWebhooks into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListRegistryWebhooks into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListRegistryWebhooks

Deserializes the ListRegistryWebhooks from a dictionary.

class databricks.sdk.service.ml.ListStreamsResponse(next_page_token: str | None = None, streams: List[Stream] | None = None)

Response to a ListStreamsRequest.

next_page_token: str | None = None

Pagination token to request the next page of results for this query.

streams: List[Stream] | None = None

List of Streams.

as_dict() → dict

Serializes the ListStreamsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListStreamsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListStreamsResponse

Deserializes the ListStreamsResponse from a dictionary.

class databricks.sdk.service.ml.ListTransitionRequestsResponse(requests: 'Optional[List[Activity]]' = None)

requests: List[Activity] | None = None

Array of open transition requests.

as_dict() → dict

Serializes the ListTransitionRequestsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ListTransitionRequestsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ListTransitionRequestsResponse

Deserializes the ListTransitionRequestsResponse from a dictionary.

class databricks.sdk.service.ml.LogBatchResponse

as_dict() → dict

Serializes the LogBatchResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LogBatchResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LogBatchResponse

Deserializes the LogBatchResponse from a dictionary.

class databricks.sdk.service.ml.LogInputsResponse

as_dict() → dict

Serializes the LogInputsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LogInputsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LogInputsResponse

Deserializes the LogInputsResponse from a dictionary.

class databricks.sdk.service.ml.LogLoggedModelParamsRequestResponse

as_dict() → dict

Serializes the LogLoggedModelParamsRequestResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LogLoggedModelParamsRequestResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LogLoggedModelParamsRequestResponse

Deserializes the LogLoggedModelParamsRequestResponse from a dictionary.

class databricks.sdk.service.ml.LogMetricResponse

as_dict() → dict

Serializes the LogMetricResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LogMetricResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LogMetricResponse

Deserializes the LogMetricResponse from a dictionary.

class databricks.sdk.service.ml.LogModelResponse

as_dict() → dict

Serializes the LogModelResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LogModelResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LogModelResponse

Deserializes the LogModelResponse from a dictionary.

class databricks.sdk.service.ml.LogOutputsResponse

as_dict() → dict

Serializes the LogOutputsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LogOutputsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LogOutputsResponse

Deserializes the LogOutputsResponse from a dictionary.

class databricks.sdk.service.ml.LogParamResponse

as_dict() → dict

Serializes the LogParamResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LogParamResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LogParamResponse

Deserializes the LogParamResponse from a dictionary.

class databricks.sdk.service.ml.LoggedModel(data: LoggedModelData | None = None, info: LoggedModelInfo | None = None)

A logged model message includes logged model attributes, tags, registration info, params, and linked run metrics.

data: LoggedModelData | None = None

The params and metrics attached to the logged model.

info: LoggedModelInfo | None = None

The logged model attributes such as model ID, status, tags, etc.

as_dict() → dict

Serializes the LoggedModel into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LoggedModel into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LoggedModel

Deserializes the LoggedModel from a dictionary.

class databricks.sdk.service.ml.LoggedModelData(metrics: List[Metric] | None = None, params: List[LoggedModelParameter] | None = None)

A LoggedModelData message includes logged model params and linked metrics.

metrics: List[Metric] | None = None

Performance metrics linked to the model.

params: List[LoggedModelParameter] | None = None

Immutable string key-value pairs of the model.

as_dict() → dict

Serializes the LoggedModelData into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LoggedModelData into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LoggedModelData

Deserializes the LoggedModelData from a dictionary.

class databricks.sdk.service.ml.LoggedModelInfo(artifact_uri: str | None = None, creation_timestamp_ms: int | None = None, creator_id: int | None = None, experiment_id: str | None = None, last_updated_timestamp_ms: int | None = None, model_id: str | None = None, model_type: str | None = None, name: str | None = None, source_run_id: str | None = None, status: LoggedModelStatus | None = None, status_message: str | None = None, tags: List[LoggedModelTag] | None = None)

A LoggedModelInfo includes logged model attributes, tags, and registration info.

artifact_uri: str | None = None

The URI of the directory where model artifacts are stored.

creation_timestamp_ms: int | None = None

The timestamp when the model was created in milliseconds since the UNIX epoch.

creator_id: int | None = None

The ID of the user or principal that created the model.

experiment_id: str | None = None

The ID of the experiment that owns the model.

last_updated_timestamp_ms: int | None = None

The timestamp when the model was last updated in milliseconds since the UNIX epoch.

model_id: str | None = None

The unique identifier for the logged model.

model_type: str | None = None

The type of model, such as "Agent", "Classifier", "LLM".

name: str | None = None

The name of the model.

source_run_id: str | None = None

The ID of the run that created the model.

status: LoggedModelStatus | None = None

The status of whether or not the model is ready for use.

status_message: str | None = None

Details on the current model status.

tags: List[LoggedModelTag] | None = None

Mutable string key-value pairs set on the model.

as_dict() → dict

Serializes the LoggedModelInfo into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LoggedModelInfo into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LoggedModelInfo

Deserializes the LoggedModelInfo from a dictionary.

class databricks.sdk.service.ml.LoggedModelParameter(key: str | None = None, value: str | None = None)

Parameter associated with a LoggedModel.

key: str | None = None

The key identifying this param.

value: str | None = None

The value of this param.

as_dict() → dict

Serializes the LoggedModelParameter into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LoggedModelParameter into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LoggedModelParameter

Deserializes the LoggedModelParameter from a dictionary.

class databricks.sdk.service.ml.LoggedModelStatus

A LoggedModelStatus enum value represents the status of a logged model.

LOGGED_MODEL_PENDING = "LOGGED_MODEL_PENDING"

LOGGED_MODEL_READY = "LOGGED_MODEL_READY"

LOGGED_MODEL_UPLOAD_FAILED = "LOGGED_MODEL_UPLOAD_FAILED"

class databricks.sdk.service.ml.LoggedModelTag(key: str | None = None, value: str | None = None)

Tag for a LoggedModel.

key: str | None = None

The tag key.

value: str | None = None

The tag value.

as_dict() → dict

Serializes the LoggedModelTag into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the LoggedModelTag into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → LoggedModelTag

Deserializes the LoggedModelTag from a dictionary.

class databricks.sdk.service.ml.MaterializedFeature(feature_name: str, cron_schedule: str | None = None, cron_schedule_trigger: CronSchedule | None = None, is_online: bool | None = None, last_materialization_time: str | None = None, materialized_feature_id: str | None = None, offline_store_config: OfflineStoreConfig | None = None, online_store_config: OnlineStoreConfig | None = None, pipeline_schedule_state: MaterializedFeaturePipelineScheduleState | None = None, streaming_mode: StreamingMode | None = None, table_name: str | None = None, table_trigger: TableTrigger | None = None)

A materialized feature represents a feature that is continuously computed and stored.

feature_name: str

The full name of the feature in Unity Catalog.

cron_schedule: str | None = None

The quartz cron expression that defines the schedule of the materialization pipeline. The schedule is evaluated in the UTC timezone.

cron_schedule_trigger: CronSchedule | None = None

A cron-based schedule trigger for the materialization pipeline.

is_online: bool | None = None

True if this is an online materialized feature. False if it is an offline materialized feature.

last_materialization_time: str | None = None

The timestamp when the pipeline last ran and updated the materialized feature values. If the pipeline has not run yet, this field will be null.

materialized_feature_id: str | None = None

Server-assigned unique identifier for the materialized feature.

offline_store_config: OfflineStoreConfig | None = None

Destination for writing feature values to an offline Delta table.

online_store_config: OnlineStoreConfig | None = None

Destination for writing feature values to an online Lakebase table.

pipeline_schedule_state: MaterializedFeaturePipelineScheduleState | None = None

The schedule state of the materialization pipeline.

streaming_mode: StreamingMode | None = None

The Structured Streaming trigger mode used for materialization. Real-time mode (RTM) targets sub-second latency for operational workloads; micro-batch mode (MBM) favors cost efficiency for ETL and analytics workloads.

table_name: str | None = None

The fully qualified Unity Catalog path to the table containing the materialized feature (Delta table or Lakebase table). Output only.

table_trigger: TableTrigger | None = None

A trigger that fires when the upstream source table changes.

as_dict() → dict

Serializes the MaterializedFeature into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the MaterializedFeature into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → MaterializedFeature

Deserializes the MaterializedFeature from a dictionary.

class databricks.sdk.service.ml.MaterializedFeaturePipelineScheduleState

ACTIVE = "ACTIVE"

PAUSED = "PAUSED"

SNAPSHOT = "SNAPSHOT"

class databricks.sdk.service.ml.MaxFunction(input: str)

Computes the maximum value.

input: str

The input column from which the maximum is computed.

as_dict() → dict

Serializes the MaxFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the MaxFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → MaxFunction

Deserializes the MaxFunction from a dictionary.

class databricks.sdk.service.ml.Metric(dataset_digest: str | None = None, dataset_name: str | None = None, key: str | None = None, model_id: str | None = None, run_id: str | None = None, step: int | None = None, timestamp: int | None = None, value: float | None = None)

Metric associated with a run, represented as a key-value pair.

dataset_digest: str | None = None

The dataset digest of the dataset associated with the metric, e.g. an md5 hash of the dataset that uniquely identifies it within datasets of the same name.

dataset_name: str | None = None

The name of the dataset associated with the metric. E.g. “my.uc.table@2” “nyc-taxi-dataset”, “fantastic-elk-3”

key: str | None = None

The key identifying the metric.

model_id: str | None = None

The ID of the logged model or registered model version associated with the metric, if applicable.

run_id: str | None = None

The ID of the run containing the metric.

step: int | None = None

The step at which the metric was logged.

timestamp: int | None = None

The timestamp at which the metric was recorded.

value: float | None = None

The value of the metric.

as_dict() → dict

Serializes the Metric into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Metric into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Metric

Deserializes the Metric from a dictionary.

class databricks.sdk.service.ml.MinFunction(input: str)

Computes the minimum value.

input: str

The input column from which the minimum is computed.

as_dict() → dict

Serializes the MinFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the MinFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → MinFunction

Deserializes the MinFunction from a dictionary.

class databricks.sdk.service.ml.Model(creation_timestamp: 'Optional[int]' = None, description: 'Optional[str]' = None, last_updated_timestamp: 'Optional[int]' = None, latest_versions: 'Optional[List[ModelVersion]]' = None, name: 'Optional[str]' = None, tags: 'Optional[List[ModelTag]]' = None, user_id: 'Optional[str]' = None)

creation_timestamp: int | None = None

Timestamp recorded when this registered_model was created.

description: str | None = None

Description of this registered_model.

last_updated_timestamp: int | None = None

Timestamp recorded when metadata for this registered_model was last updated.

latest_versions: List[ModelVersion] | None = None

Collection of latest model versions for each stage. Only contains models with current READY status.

name: str | None = None

Unique name for the model.

tags: List[ModelTag] | None = None

Tags: Additional metadata key-value pairs for this registered_model.

user_id: str | None = None

User that created this registered_model

as_dict() → dict

Serializes the Model into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Model into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Model

Deserializes the Model from a dictionary.

class databricks.sdk.service.ml.ModelDatabricks(creation_timestamp: 'Optional[int]' = None, description: 'Optional[str]' = None, id: 'Optional[str]' = None, last_updated_timestamp: 'Optional[int]' = None, latest_versions: 'Optional[List[ModelVersion]]' = None, name: 'Optional[str]' = None, permission_level: 'Optional[PermissionLevel]' = None, tags: 'Optional[List[ModelTag]]' = None, user_id: 'Optional[str]' = None)

creation_timestamp: int | None = None

Creation time of the object, as a Unix timestamp in milliseconds.

description: str | None = None

User-specified description for the object.

id: str | None = None

Unique identifier for the object.

last_updated_timestamp: int | None = None

Last update time of the object, as a Unix timestamp in milliseconds.

latest_versions: List[ModelVersion] | None = None

Array of model versions, each the latest version for its stage.

name: str | None = None

Name of the model.

permission_level: PermissionLevel | None = None

Permission level granted for the requesting user on this registered model

tags: List[ModelTag] | None = None

Array of tags associated with the model.

user_id: str | None = None

The username of the user that created the object.

as_dict() → dict

Serializes the ModelDatabricks into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ModelDatabricks into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ModelDatabricks

Deserializes the ModelDatabricks from a dictionary.

class databricks.sdk.service.ml.ModelInput(model_id: str)

Represents a LoggedModel or Registered Model Version input to a Run.

model_id: str

The unique identifier of the model.

as_dict() → dict

Serializes the ModelInput into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ModelInput into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ModelInput

Deserializes the ModelInput from a dictionary.

class databricks.sdk.service.ml.ModelOutput(model_id: str, step: int)

Represents a LoggedModel output of a Run.

model_id: str

The unique identifier of the model.

step: int

The step at which the model was produced.

as_dict() → dict

Serializes the ModelOutput into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ModelOutput into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ModelOutput

Deserializes the ModelOutput from a dictionary.

class databricks.sdk.service.ml.ModelTag(key: str | None = None, value: str | None = None)

Tag for a registered model

key: str | None = None

The tag key.

value: str | None = None

The tag value.

as_dict() → dict

Serializes the ModelTag into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ModelTag into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ModelTag

Deserializes the ModelTag from a dictionary.

class databricks.sdk.service.ml.ModelVersion(creation_timestamp: 'Optional[int]' = None, current_stage: 'Optional[str]' = None, description: 'Optional[str]' = None, last_updated_timestamp: 'Optional[int]' = None, name: 'Optional[str]' = None, run_id: 'Optional[str]' = None, run_link: 'Optional[str]' = None, source: 'Optional[str]' = None, status: 'Optional[ModelVersionStatus]' = None, status_message: 'Optional[str]' = None, tags: 'Optional[List[ModelVersionTag]]' = None, user_id: 'Optional[str]' = None, version: 'Optional[str]' = None)

creation_timestamp: int | None = None

Timestamp recorded when this model_version was created.

current_stage: str | None = None

Current stage for this model_version.

description: str | None = None

Description of this model_version.

last_updated_timestamp: int | None = None

Timestamp recorded when metadata for this model_version was last updated.

name: str | None = None

Unique name of the model

run_id: str | None = None

MLflow run ID used when creating model_version, if source was generated by an experiment run stored in MLflow tracking server.

run_link: str | None = None

Run Link: Direct link to the run that generated this version

source: str | None = None

URI indicating the location of the source model artifacts, used when creating model_version

status: ModelVersionStatus | None = None

Current status of model_version

status_message: str | None = None

Details on current status, if it is pending or failed.

tags: List[ModelVersionTag] | None = None

Tags: Additional metadata key-value pairs for this model_version.

user_id: str | None = None

User that created this model_version.

version: str | None = None

Model’s version number.

as_dict() → dict

Serializes the ModelVersion into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ModelVersion into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ModelVersion

Deserializes the ModelVersion from a dictionary.

class databricks.sdk.service.ml.ModelVersionDatabricks(creation_timestamp: 'Optional[int]' = None, current_stage: 'Optional[str]' = None, description: 'Optional[str]' = None, email_subscription_status: 'Optional[RegistryEmailSubscriptionType]' = None, feature_list: 'Optional[FeatureList]' = None, last_updated_timestamp: 'Optional[int]' = None, name: 'Optional[str]' = None, open_requests: 'Optional[List[Activity]]' = None, permission_level: 'Optional[PermissionLevel]' = None, run_id: 'Optional[str]' = None, run_link: 'Optional[str]' = None, source: 'Optional[str]' = None, status: 'Optional[Status]' = None, status_message: 'Optional[str]' = None, tags: 'Optional[List[ModelVersionTag]]' = None, user_id: 'Optional[str]' = None, version: 'Optional[str]' = None)

creation_timestamp: int | None = None

Creation time of the object, as a Unix timestamp in milliseconds.

current_stage: str | None = None

description: str | None = None

User-specified description for the object.

email_subscription_status: RegistryEmailSubscriptionType | None = None

Email Subscription Status: This is the subscription status of the user to the model version Users get subscribed by interacting with the model version.

feature_list: FeatureList | None = None

Feature lineage of model_version.

last_updated_timestamp: int | None = None

Time of the object at last update, as a Unix timestamp in milliseconds.

name: str | None = None

Name of the model.

open_requests: List[Activity] | None = None

Open requests for this model_versions. Gap in sequence number is intentional and is done in order to match field sequence numbers of ModelVersion proto message

permission_level: PermissionLevel | None = None

run_id: str | None = None

Unique identifier for the MLflow tracking run associated with the source model artifacts.

run_link: str | None = None

URL of the run associated with the model artifacts. This field is set at model version creation time only for model versions whose source run is from a tracking server that is different from the registry server.

source: str | None = None

URI that indicates the location of the source model artifacts. This is used when creating the model version.

status: Status | None = None

status_message: str | None = None

Details on the current status, for example why registration failed.

tags: List[ModelVersionTag] | None = None

Array of tags that are associated with the model version.

user_id: str | None = None

The username of the user that created the object.

version: str | None = None

Version of the model.

as_dict() → dict

Serializes the ModelVersionDatabricks into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ModelVersionDatabricks into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ModelVersionDatabricks

Deserializes the ModelVersionDatabricks from a dictionary.

class databricks.sdk.service.ml.ModelVersionStatus

The status of the model version. Valid values are: * PENDING_REGISTRATION: Request to register a new model version is pending as server performs background tasks. * FAILED_REGISTRATION: Request to register a new model version has failed. * READY: Model version is ready for use.

FAILED_REGISTRATION = "FAILED_REGISTRATION"

PENDING_REGISTRATION = "PENDING_REGISTRATION"

READY = "READY"

class databricks.sdk.service.ml.ModelVersionTag(key: 'Optional[str]' = None, value: 'Optional[str]' = None)

key: str | None = None

The tag key.

value: str | None = None

The tag value.

as_dict() → dict

Serializes the ModelVersionTag into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the ModelVersionTag into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → ModelVersionTag

Deserializes the ModelVersionTag from a dictionary.

class databricks.sdk.service.ml.MtlsConfig(keystore_location: str, keystore_password_ref: SecretScopeReference, key_password_ref: SecretScopeReference, truststore_location: str, truststore_password_ref: SecretScopeReference, disable_hostname_verification: bool | None = None)

Mutual-TLS (mTLS) authentication configuration. The keystore (client certificate + private key) and truststore (CAs trusted to verify the broker) live as JKS files on Unity Catalog volumes, with their passwords stored in Databricks secret scopes. This matches the SSL setup pattern documented at https://docs.databricks.com/en/connect/streaming/kafka/authentication#use-ssl-to-connect-databricks-to-kafka.

At materialization time, the generated PySpark code passes the JKS file paths and resolved passwords through to the Kafka SSL options (kafka.ssl.keystore.location, kafka.ssl.keystore.password, kafka.ssl.key.password, kafka.ssl.truststore.location, kafka.ssl.truststore.password). Passwords are resolved on the Spark cluster via dbutils.secrets.get; this message stores only references, never password values.

keystore_location: str

Unity Catalog volume path to the JKS keystore file containing the client certificate and private key. e.g. “/Volumes/<catalog>/<schema>/<volume>/client.jks”. The materialization compute must have read permission on this volume.

keystore_password_ref: SecretScopeReference

Secret-scope reference for the JKS keystore password.

key_password_ref: SecretScopeReference

Secret-scope reference for the private key password. Often the same value as the keystore password (keytool’s default), but provided as a separate field because Apache Kafka requires it as a distinct option (kafka.ssl.key.password).

truststore_location: str

Unity Catalog volume path to the JKS truststore file containing the CA certificate(s) trusted to verify the Kafka broker’s server certificate. e.g. “/Volumes/<catalog>/<schema>/<volume>/truststore.jks”.

truststore_password_ref: SecretScopeReference

Secret-scope reference for the JKS truststore password.

disable_hostname_verification: bool | None = None

Set to true only when the broker certificate’s SAN intentionally does not match the connection endpoint — for example when reaching the cluster through a PrivateLink endpoint whose DNS name is not in the broker certificate. Skipping the hostname check removes a defense against man-in-the-middle attacks; do not enable casually. mTLS client authentication is unaffected by this option.

See the Apache Kafka SSL security guide for background on this check: https://kafka.apache.org/42/security/encryption-and-authentication-using-ssl/#host-name-verification

as_dict() → dict

Serializes the MtlsConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the MtlsConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → MtlsConfig

Deserializes the MtlsConfig from a dictionary.

class databricks.sdk.service.ml.OfflineStoreConfig(catalog_name: str, schema_name: str, table_name_prefix: str)

Configuration for offline store destination.

catalog_name: str

The Unity Catalog catalog name.

schema_name: str

The Unity Catalog schema name.

table_name_prefix: str

Prefix for Unity Catalog table name. The materialized feature will be stored in a table with this prefix and a generated postfix.

as_dict() → dict

Serializes the OfflineStoreConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the OfflineStoreConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → OfflineStoreConfig

Deserializes the OfflineStoreConfig from a dictionary.

class databricks.sdk.service.ml.OnlineStore(name: str, capacity: str, creation_time: str | None = None, creator: str | None = None, read_replica_count: int | None = None, state: OnlineStoreState | None = None, usage_policy_id: str | None = None)

An OnlineStore is a logical database instance that stores and serves features online.

name: str

The name of the online store. This is the unique identifier for the online store.

capacity: str

The capacity of the online store. Valid values are “CU_1”, “CU_2”, “CU_4”, “CU_8”.

creation_time: str | None = None

The timestamp when the online store was created.

creator: str | None = None

The email of the creator of the online store.

read_replica_count: int | None = None

The number of read replicas for the online store. Defaults to 0.

state: OnlineStoreState | None = None

The current state of the online store.

usage_policy_id: str | None = None

The usage policy applied to the online store to track billing.

as_dict() → dict

Serializes the OnlineStore into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the OnlineStore into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → OnlineStore

Deserializes the OnlineStore from a dictionary.

class databricks.sdk.service.ml.OnlineStoreConfig(catalog_name: str, schema_name: str, table_name_prefix: str, online_store_name: str)

Configuration for online store destination.

catalog_name: str

The Unity Catalog catalog name. This name is also used as the Lakebase logical database name. Quoting is handled by the backend where needed, do not pre-quote it.

schema_name: str

The Unity Catalog schema name. This name is also used as the Lakebase schema name under the database. Quoting is handled by the backend where needed, do not pre-quote it.

table_name_prefix: str

Prefix for Unity Catalog table name. The materialized feature will be stored in a Lakebase table with this prefix and a generated postfix.

online_store_name: str

The name of the target online store.

as_dict() → dict

Serializes the OnlineStoreConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the OnlineStoreConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → OnlineStoreConfig

Deserializes the OnlineStoreConfig from a dictionary.

class databricks.sdk.service.ml.OnlineStoreState

AVAILABLE = "AVAILABLE"

DELETING = "DELETING"

FAILING_OVER = "FAILING_OVER"

STARTING = "STARTING"

STOPPED = "STOPPED"

UPDATING = "UPDATING"

class databricks.sdk.service.ml.Param(key: str | None = None, value: str | None = None)

Param associated with a run.

key: str | None = None

Key identifying this param.

value: str | None = None

Value associated with this param.

as_dict() → dict

Serializes the Param into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Param into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Param

Deserializes the Param from a dictionary.

class databricks.sdk.service.ml.PermissionLevel

Permission level of the requesting user on the object. For what is allowed at each level, see [MLflow Model permissions](..).

CAN_CREATE_REGISTERED_MODEL = "CAN_CREATE_REGISTERED_MODEL"

CAN_EDIT = "CAN_EDIT"

CAN_MANAGE = "CAN_MANAGE"

CAN_MANAGE_PRODUCTION_VERSIONS = "CAN_MANAGE_PRODUCTION_VERSIONS"

CAN_MANAGE_STAGING_VERSIONS = "CAN_MANAGE_STAGING_VERSIONS"

CAN_READ = "CAN_READ"

class databricks.sdk.service.ml.PublishSpec(online_store: 'str', online_table_name: 'str', publish_mode: 'PublishSpecPublishMode')

online_store: str

The name of the target online store.

online_table_name: str

The full three-part (catalog, schema, table) name of the online table.

publish_mode: PublishSpecPublishMode

The publish mode of the pipeline that syncs the online table with the source table.

as_dict() → dict

Serializes the PublishSpec into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the PublishSpec into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → PublishSpec

Deserializes the PublishSpec from a dictionary.

class databricks.sdk.service.ml.PublishSpecPublishMode

CONTINUOUS = "CONTINUOUS"

SNAPSHOT = "SNAPSHOT"

TRIGGERED = "TRIGGERED"

class databricks.sdk.service.ml.PublishTableResponse(online_table_name: 'Optional[str]' = None, pipeline_id: 'Optional[str]' = None)

online_table_name: str | None = None

The full three-part (catalog, schema, table) name of the online table.

pipeline_id: str | None = None

The ID of the pipeline that syncs the online table with the source table.

as_dict() → dict

Serializes the PublishTableResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the PublishTableResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → PublishTableResponse

Deserializes the PublishTableResponse from a dictionary.

class databricks.sdk.service.ml.RegisteredModelAccessControlRequest(group_name: 'Optional[str]' = None, permission_level: 'Optional[RegisteredModelPermissionLevel]' = None, service_principal_name: 'Optional[str]' = None, user_name: 'Optional[str]' = None)

group_name: str | None = None

name of the group

permission_level: RegisteredModelPermissionLevel | None = None

service_principal_name: str | None = None

application ID of a service principal

user_name: str | None = None

name of the user

as_dict() → dict

Serializes the RegisteredModelAccessControlRequest into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RegisteredModelAccessControlRequest into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RegisteredModelAccessControlRequest

Deserializes the RegisteredModelAccessControlRequest from a dictionary.

class databricks.sdk.service.ml.RegisteredModelAccessControlResponse(all_permissions: 'Optional[List[RegisteredModelPermission]]' = None, display_name: 'Optional[str]' = None, group_name: 'Optional[str]' = None, service_principal_name: 'Optional[str]' = None, user_name: 'Optional[str]' = None)

all_permissions: List[RegisteredModelPermission] | None = None

All permissions.

display_name: str | None = None

Display name of the user or service principal.

group_name: str | None = None

name of the group

service_principal_name: str | None = None

Name of the service principal.

user_name: str | None = None

name of the user

as_dict() → dict

Serializes the RegisteredModelAccessControlResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RegisteredModelAccessControlResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RegisteredModelAccessControlResponse

Deserializes the RegisteredModelAccessControlResponse from a dictionary.

class databricks.sdk.service.ml.RegisteredModelPermission(inherited: 'Optional[bool]' = None, inherited_from_object: 'Optional[List[str]]' = None, permission_level: 'Optional[RegisteredModelPermissionLevel]' = None)

inherited: bool | None = None

inherited_from_object: List[str] | None = None

permission_level: RegisteredModelPermissionLevel | None = None

as_dict() → dict

Serializes the RegisteredModelPermission into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RegisteredModelPermission into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RegisteredModelPermission

Deserializes the RegisteredModelPermission from a dictionary.

class databricks.sdk.service.ml.RegisteredModelPermissionLevel

Permission level

CAN_EDIT = "CAN_EDIT"

CAN_MANAGE = "CAN_MANAGE"

CAN_MANAGE_PRODUCTION_VERSIONS = "CAN_MANAGE_PRODUCTION_VERSIONS"

CAN_MANAGE_STAGING_VERSIONS = "CAN_MANAGE_STAGING_VERSIONS"

CAN_READ = "CAN_READ"

class databricks.sdk.service.ml.RegisteredModelPermissions(access_control_list: 'Optional[List[RegisteredModelAccessControlResponse]]' = None, object_id: 'Optional[str]' = None, object_type: 'Optional[str]' = None)

access_control_list: List[RegisteredModelAccessControlResponse] | None = None

object_id: str | None = None

object_type: str | None = None

as_dict() → dict

Serializes the RegisteredModelPermissions into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RegisteredModelPermissions into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RegisteredModelPermissions

Deserializes the RegisteredModelPermissions from a dictionary.

class databricks.sdk.service.ml.RegisteredModelPermissionsDescription(description: 'Optional[str]' = None, permission_level: 'Optional[RegisteredModelPermissionLevel]' = None)

description: str | None = None

permission_level: RegisteredModelPermissionLevel | None = None

as_dict() → dict

Serializes the RegisteredModelPermissionsDescription into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RegisteredModelPermissionsDescription into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RegisteredModelPermissionsDescription

Deserializes the RegisteredModelPermissionsDescription from a dictionary.

class databricks.sdk.service.ml.RegistryEmailSubscriptionType

Note

Experimental: This entity may change or be removed in a future release without warning. Email subscription types for registry notifications: - ALL_EVENTS: Subscribed to all events. - DEFAULT: Default subscription type. - SUBSCRIBED: Subscribed to notifications. - UNSUBSCRIBED: Not subscribed to notifications.

ALL_EVENTS = "ALL_EVENTS"

DEFAULT = "DEFAULT"

SUBSCRIBED = "SUBSCRIBED"

UNSUBSCRIBED = "UNSUBSCRIBED"

class databricks.sdk.service.ml.RegistryWebhook(creation_timestamp: 'Optional[int]' = None, description: 'Optional[str]' = None, events: 'Optional[List[RegistryWebhookEvent]]' = None, http_url_spec: 'Optional[HttpUrlSpecWithoutSecret]' = None, id: 'Optional[str]' = None, job_spec: 'Optional[JobSpecWithoutSecret]' = None, last_updated_timestamp: 'Optional[int]' = None, model_name: 'Optional[str]' = None, status: 'Optional[RegistryWebhookStatus]' = None)

creation_timestamp: int | None = None

Creation time of the object, as a Unix timestamp in milliseconds.

description: str | None = None

User-specified description for the webhook.

events: List[RegistryWebhookEvent] | None = None

Events that can trigger a registry webhook: * MODEL_VERSION_CREATED: A new model version was created for the associated model.

• MODEL_VERSION_TRANSITIONED_STAGE: A model version’s stage was changed.

• TRANSITION_REQUEST_CREATED: A user requested a model version’s stage be transitioned.

• COMMENT_CREATED: A user wrote a comment on a registered model.

• REGISTERED_MODEL_CREATED: A new registered model was created. This event type can only be

specified for a registry-wide webhook, which can be created by not specifying a model name in the create request.

• MODEL_VERSION_TAG_SET: A user set a tag on the model version.

• MODEL_VERSION_TRANSITIONED_TO_STAGING: A model version was transitioned to staging.

• MODEL_VERSION_TRANSITIONED_TO_PRODUCTION: A model version was transitioned to production.

• MODEL_VERSION_TRANSITIONED_TO_ARCHIVED: A model version was archived.

• TRANSITION_REQUEST_TO_STAGING_CREATED: A user requested a model version be transitioned to

staging.

• TRANSITION_REQUEST_TO_PRODUCTION_CREATED: A user requested a model version be transitioned

to production.

• TRANSITION_REQUEST_TO_ARCHIVED_CREATED: A user requested a model version be archived.

http_url_spec: HttpUrlSpecWithoutSecret | None = None

id: str | None = None

Webhook ID

job_spec: JobSpecWithoutSecret | None = None

last_updated_timestamp: int | None = None

Time of the object at last update, as a Unix timestamp in milliseconds.

model_name: str | None = None

Name of the model whose events would trigger this webhook.

status: RegistryWebhookStatus | None = None

as_dict() → dict

Serializes the RegistryWebhook into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RegistryWebhook into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RegistryWebhook

Deserializes the RegistryWebhook from a dictionary.

class databricks.sdk.service.ml.RegistryWebhookEvent

COMMENT_CREATED = "COMMENT_CREATED"

MODEL_VERSION_CREATED = "MODEL_VERSION_CREATED"

MODEL_VERSION_TAG_SET = "MODEL_VERSION_TAG_SET"

MODEL_VERSION_TRANSITIONED_STAGE = "MODEL_VERSION_TRANSITIONED_STAGE"

MODEL_VERSION_TRANSITIONED_TO_ARCHIVED = "MODEL_VERSION_TRANSITIONED_TO_ARCHIVED"

MODEL_VERSION_TRANSITIONED_TO_PRODUCTION = "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"

MODEL_VERSION_TRANSITIONED_TO_STAGING = "MODEL_VERSION_TRANSITIONED_TO_STAGING"

REGISTERED_MODEL_CREATED = "REGISTERED_MODEL_CREATED"

TRANSITION_REQUEST_CREATED = "TRANSITION_REQUEST_CREATED"

TRANSITION_REQUEST_TO_ARCHIVED_CREATED = "TRANSITION_REQUEST_TO_ARCHIVED_CREATED"

TRANSITION_REQUEST_TO_PRODUCTION_CREATED = "TRANSITION_REQUEST_TO_PRODUCTION_CREATED"

TRANSITION_REQUEST_TO_STAGING_CREATED = "TRANSITION_REQUEST_TO_STAGING_CREATED"

class databricks.sdk.service.ml.RegistryWebhookStatus

Enable or disable triggering the webhook, or put the webhook into test mode. The default is ACTIVE: * ACTIVE: Webhook is triggered when an associated event happens. * DISABLED: Webhook is not triggered. * TEST_MODE: Webhook can be triggered through the test endpoint, but is not triggered on a real event.

ACTIVE = "ACTIVE"

DISABLED = "DISABLED"

TEST_MODE = "TEST_MODE"

class databricks.sdk.service.ml.RejectTransitionRequestResponse(activity: 'Optional[Activity]' = None)

activity: Activity | None = None

New activity generated as a result of this operation.

as_dict() → dict

Serializes the RejectTransitionRequestResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RejectTransitionRequestResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RejectTransitionRequestResponse

Deserializes the RejectTransitionRequestResponse from a dictionary.

class databricks.sdk.service.ml.RenameModelResponse(registered_model: 'Optional[Model]' = None)

registered_model: Model | None = None

as_dict() → dict

Serializes the RenameModelResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RenameModelResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RenameModelResponse

Deserializes the RenameModelResponse from a dictionary.

class databricks.sdk.service.ml.RequestSource(flat_schema: FlatSchema | None = None)

A request-time data source whose value is provided at inference time: offline batch scoring or online serving endpoint

flat_schema: FlatSchema | None = None

A flat schema with scalar-typed fields only.

as_dict() → dict

Serializes the RequestSource into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RequestSource into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RequestSource

Deserializes the RequestSource from a dictionary.

class databricks.sdk.service.ml.RestoreExperimentResponse

as_dict() → dict

Serializes the RestoreExperimentResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RestoreExperimentResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RestoreExperimentResponse

Deserializes the RestoreExperimentResponse from a dictionary.

class databricks.sdk.service.ml.RestoreRunResponse

as_dict() → dict

Serializes the RestoreRunResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RestoreRunResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RestoreRunResponse

Deserializes the RestoreRunResponse from a dictionary.

class databricks.sdk.service.ml.RestoreRunsResponse(runs_restored: 'Optional[int]' = None)

runs_restored: int | None = None

The number of runs restored.

as_dict() → dict

Serializes the RestoreRunsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RestoreRunsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RestoreRunsResponse

Deserializes the RestoreRunsResponse from a dictionary.

class databricks.sdk.service.ml.RollingWindow(window_duration: Duration, delay: Duration | None = None)

A rolling time window with an optional delay. This is the SQL-spec-aligned replacement for ContinuousWindow: delay is the non-negative counterpart of the legacy non-positive ContinuousWindow.offset.

window_duration: Duration

The duration of the rolling window (must be positive).

delay: Duration | None = None

The delay applied to the end of the rolling window (must be non-negative). For example, delay=1d shifts the window end 1 day before the evaluation time.

as_dict() → dict

Serializes the RollingWindow into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RollingWindow into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RollingWindow

Deserializes the RollingWindow from a dictionary.

class databricks.sdk.service.ml.Run(data: RunData | None = None, info: RunInfo | None = None, inputs: RunInputs | None = None)

A single run.

data: RunData | None = None

Run data.

info: RunInfo | None = None

Run metadata.

inputs: RunInputs | None = None

Run inputs.

as_dict() → dict

Serializes the Run into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Run into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Run

Deserializes the Run from a dictionary.

class databricks.sdk.service.ml.RunData(metrics: List[Metric] | None = None, params: List[Param] | None = None, tags: List[RunTag] | None = None)

Run data (metrics, params, and tags).

metrics: List[Metric] | None = None

Run metrics.

params: List[Param] | None = None

Run parameters.

tags: List[RunTag] | None = None

Additional metadata key-value pairs.

as_dict() → dict

Serializes the RunData into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RunData into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RunData

Deserializes the RunData from a dictionary.

class databricks.sdk.service.ml.RunInfo(artifact_uri: str | None = None, end_time: int | None = None, experiment_id: str | None = None, lifecycle_stage: str | None = None, run_id: str | None = None, run_name: str | None = None, run_uuid: str | None = None, start_time: int | None = None, status: RunInfoStatus | None = None, user_id: str | None = None)

Metadata of a single run.

artifact_uri: str | None = None

URI of the directory where artifacts should be uploaded. This can be a local path (starting with “/”), or a distributed file system (DFS) path, like s3://bucket/directory or dbfs:/my/directory. If not set, the local ./mlruns directory is chosen.

end_time: int | None = None

Unix timestamp of when the run ended in milliseconds.

experiment_id: str | None = None

The experiment ID.

lifecycle_stage: str | None = None

Current life cycle stage of the experiment : OneOf(“active”, “deleted”)

run_id: str | None = None

Unique identifier for the run.

run_name: str | None = None

The name of the run.

run_uuid: str | None = None

[Deprecated, use run_id instead] Unique identifier for the run. This field will be removed in a future MLflow version.

start_time: int | None = None

Unix timestamp of when the run started in milliseconds.

status: RunInfoStatus | None = None

Current status of the run.

user_id: str | None = None

User who initiated the run. This field is deprecated as of MLflow 1.0, and will be removed in a future MLflow release. Use ‘mlflow.user’ tag instead.

as_dict() → dict

Serializes the RunInfo into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RunInfo into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RunInfo

Deserializes the RunInfo from a dictionary.

class databricks.sdk.service.ml.RunInfoStatus

Status of a run.

FAILED = "FAILED"

FINISHED = "FINISHED"

KILLED = "KILLED"

RUNNING = "RUNNING"

SCHEDULED = "SCHEDULED"

class databricks.sdk.service.ml.RunInputs(dataset_inputs: List[DatasetInput] | None = None, model_inputs: List[ModelInput] | None = None)

Run inputs.

dataset_inputs: List[DatasetInput] | None = None

Run metrics.

model_inputs: List[ModelInput] | None = None

Model inputs to the Run.

as_dict() → dict

Serializes the RunInputs into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RunInputs into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RunInputs

Deserializes the RunInputs from a dictionary.

class databricks.sdk.service.ml.RunTag(key: str | None = None, value: str | None = None)

Tag for a run.

key: str | None = None

The tag key.

value: str | None = None

The tag value.

as_dict() → dict

Serializes the RunTag into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the RunTag into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → RunTag

Deserializes the RunTag from a dictionary.

class databricks.sdk.service.ml.ScalarDataType

Scalar data types for request-time field definitions. Only flat (non-nested) types are supported.

BINARY = "BINARY"

BOOLEAN = "BOOLEAN"

DATE = "DATE"

DECIMAL = "DECIMAL"

DOUBLE = "DOUBLE"

FLOAT = "FLOAT"

INTEGER = "INTEGER"

LONG = "LONG"

SHORT = "SHORT"

STRING = "STRING"

TIMESTAMP = "TIMESTAMP"

class databricks.sdk.service.ml.SchemaConfig(json_schema: 'Optional[str]' = None)

json_schema: str | None = None

Schema of the JSON object in standard IETF JSON schema format (https://json-schema.org/).

as_dict() → dict

Serializes the SchemaConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SchemaConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SchemaConfig

Deserializes the SchemaConfig from a dictionary.

class databricks.sdk.service.ml.SearchExperimentsResponse(experiments: 'Optional[List[Experiment]]' = None, next_page_token: 'Optional[str]' = None)

experiments: List[Experiment] | None = None

Experiments that match the search criteria

next_page_token: str | None = None

Token that can be used to retrieve the next page of experiments. An empty token means that no more experiments are available for retrieval.

as_dict() → dict

Serializes the SearchExperimentsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SearchExperimentsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SearchExperimentsResponse

Deserializes the SearchExperimentsResponse from a dictionary.

class databricks.sdk.service.ml.SearchLoggedModelsDataset(dataset_name: 'str', dataset_digest: 'Optional[str]' = None)

dataset_name: str

The name of the dataset.

dataset_digest: str | None = None

The digest of the dataset.

as_dict() → dict

Serializes the SearchLoggedModelsDataset into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SearchLoggedModelsDataset into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SearchLoggedModelsDataset

Deserializes the SearchLoggedModelsDataset from a dictionary.

class databricks.sdk.service.ml.SearchLoggedModelsOrderBy(field_name: 'str', ascending: 'Optional[bool]' = None, dataset_digest: 'Optional[str]' = None, dataset_name: 'Optional[str]' = None)

field_name: str

The name of the field to order by, e.g. “metrics.accuracy”.

ascending: bool | None = None

Whether the search results order is ascending or not.

dataset_digest: str | None = None

If field_name refers to a metric, this field specifies the digest of the dataset associated with the metric. Only metrics associated with the specified dataset name and digest will be considered for ordering. This field may only be set if dataset_name is also set.

dataset_name: str | None = None

If field_name refers to a metric, this field specifies the name of the dataset associated with the metric. Only metrics associated with the specified dataset name will be considered for ordering. This field may only be set if field_name refers to a metric.

as_dict() → dict

Serializes the SearchLoggedModelsOrderBy into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SearchLoggedModelsOrderBy into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SearchLoggedModelsOrderBy

Deserializes the SearchLoggedModelsOrderBy from a dictionary.

class databricks.sdk.service.ml.SearchLoggedModelsResponse(models: 'Optional[List[LoggedModel]]' = None, next_page_token: 'Optional[str]' = None)

models: List[LoggedModel] | None = None

Logged models that match the search criteria.

next_page_token: str | None = None

The token that can be used to retrieve the next page of logged models.

as_dict() → dict

Serializes the SearchLoggedModelsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SearchLoggedModelsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SearchLoggedModelsResponse

Deserializes the SearchLoggedModelsResponse from a dictionary.

class databricks.sdk.service.ml.SearchModelVersionsResponse(model_versions: 'Optional[List[ModelVersion]]' = None, next_page_token: 'Optional[str]' = None)

model_versions: List[ModelVersion] | None = None

Models that match the search criteria

next_page_token: str | None = None

Pagination token to request next page of models for the same search query.

as_dict() → dict

Serializes the SearchModelVersionsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SearchModelVersionsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SearchModelVersionsResponse

Deserializes the SearchModelVersionsResponse from a dictionary.

class databricks.sdk.service.ml.SearchModelsResponse(next_page_token: 'Optional[str]' = None, registered_models: 'Optional[List[Model]]' = None)

next_page_token: str | None = None

Pagination token to request the next page of models.

registered_models: List[Model] | None = None

Registered Models that match the search criteria.

as_dict() → dict

Serializes the SearchModelsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SearchModelsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SearchModelsResponse

Deserializes the SearchModelsResponse from a dictionary.

class databricks.sdk.service.ml.SearchRunsResponse(next_page_token: 'Optional[str]' = None, runs: 'Optional[List[Run]]' = None)

next_page_token: str | None = None

Token for the next page of runs.

runs: List[Run] | None = None

Runs that match the search criteria.

as_dict() → dict

Serializes the SearchRunsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SearchRunsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SearchRunsResponse

Deserializes the SearchRunsResponse from a dictionary.

class databricks.sdk.service.ml.SecretScopeReference(scope: str, key: str)

Reference to an entry in a Databricks secret scope. The referenced value is fetched on the Spark cluster at materialization time via dbutils.secrets.get(scope, key).

scope: str

The Databricks secret scope name.

key: str

The key within the scope.

as_dict() → dict

Serializes the SecretScopeReference into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SecretScopeReference into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SecretScopeReference

Deserializes the SecretScopeReference from a dictionary.

class databricks.sdk.service.ml.SetExperimentTagResponse

as_dict() → dict

Serializes the SetExperimentTagResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SetExperimentTagResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SetExperimentTagResponse

Deserializes the SetExperimentTagResponse from a dictionary.

class databricks.sdk.service.ml.SetLoggedModelTagsResponse

as_dict() → dict

Serializes the SetLoggedModelTagsResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SetLoggedModelTagsResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SetLoggedModelTagsResponse

Deserializes the SetLoggedModelTagsResponse from a dictionary.

class databricks.sdk.service.ml.SetModelTagResponse

as_dict() → dict

Serializes the SetModelTagResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SetModelTagResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SetModelTagResponse

Deserializes the SetModelTagResponse from a dictionary.

class databricks.sdk.service.ml.SetModelVersionTagResponse

as_dict() → dict

Serializes the SetModelVersionTagResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SetModelVersionTagResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SetModelVersionTagResponse

Deserializes the SetModelVersionTagResponse from a dictionary.

class databricks.sdk.service.ml.SetTagResponse

as_dict() → dict

Serializes the SetTagResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SetTagResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SetTagResponse

Deserializes the SetTagResponse from a dictionary.

class databricks.sdk.service.ml.SlidingWindow(window_duration: 'str', slide_duration: 'str')

window_duration: str

The duration of the sliding window.

slide_duration: str

The slide duration (interval by which windows advance, must be positive and less than duration).

as_dict() → dict

Serializes the SlidingWindow into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SlidingWindow into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SlidingWindow

Deserializes the SlidingWindow from a dictionary.

class databricks.sdk.service.ml.Status

The status of the model version. Valid values are: * PENDING_REGISTRATION: Request to register a new model version is pending as server performs background tasks. * FAILED_REGISTRATION: Request to register a new model version has failed. * READY: Model version is ready for use.

FAILED_REGISTRATION = "FAILED_REGISTRATION"

PENDING_REGISTRATION = "PENDING_REGISTRATION"

READY = "READY"

class databricks.sdk.service.ml.StddevPopFunction(input: str)

Computes the population standard deviation.

input: str

The input column from which the population standard deviation is computed. For Kafka sources, use dot-prefixed path notation (e.g., “value.amount”). For nested fields, the leaf node name is used. TODO(FS-939): Colon-prefixed notation (e.g., “value:amount”) is supported for backwards compatibility but is deprecated; migrate to dot notation.

as_dict() → dict

Serializes the StddevPopFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the StddevPopFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → StddevPopFunction

Deserializes the StddevPopFunction from a dictionary.

class databricks.sdk.service.ml.StddevSampFunction(input: str)

Computes the sample standard deviation.

input: str

The input column from which the sample standard deviation is computed.

as_dict() → dict

Serializes the StddevSampFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the StddevSampFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → StddevSampFunction

Deserializes the StddevSampFunction from a dictionary.

class databricks.sdk.service.ml.Stream(name: str, source_config: StreamSourceConfig, connection_config: StreamConnectionConfig, schema_config: StreamSchemaConfig, ingestion_config: IngestionConfig, browse_only: bool | None = None, create_time: Timestamp | None = None, created_by: str | None = None, description: str | None = None, update_time: Timestamp | None = None, updated_by: str | None = None)

A Stream is a governed UC entity representing an external streaming data source. The source_config oneof determines the streaming platform source (e.g. Kafka, Kinesis, etc.).

name: str

Full three-part (catalog.schema.stream) name of the stream.

source_config: StreamSourceConfig

Source-specific configuration. Determines the streaming platform source.

connection_config: StreamConnectionConfig

Specifies how to connect and authenticate to the stream platform.

schema_config: StreamSchemaConfig

Schema definitions for the stream. Currently only direct schemas are supported. In a future milestone, we will support schema registries through a UC Connection.

ingestion_config: IngestionConfig

Configuration for streaming data ingestion: the managed table storing an offline copy of forward fill data and optional historical backfill.

browse_only: bool | None = None

Indicates whether the principal is limited to retrieving metadata for the associated object through the BROWSE privilege when include_browse is enabled in the request.

create_time: Timestamp | None = None

Time at which this Stream was created.

created_by: str | None = None

Username of the Stream creator.

description: str | None = None

User-provided description.

update_time: Timestamp | None = None

Time at which this Stream was last modified.

updated_by: str | None = None

Username of user who last modified the Stream.

as_dict() → dict

Serializes the Stream into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the Stream into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → Stream

Deserializes the Stream from a dictionary.

class databricks.sdk.service.ml.StreamConnectionConfig(direct_mtls_config: DirectMtlsConfig | None = None, uc_connection_name: str | None = None)

Specifies how to connect and authenticate to the stream platform.

direct_mtls_config: DirectMtlsConfig | None = None

Direct mTLS configuration for stream platform access. This is only used in the short term until UC Kafka Connections support mTLS (XTA-18030). Once UC Kafka Connections support mTLS, this will be deprecated.

uc_connection_name: str | None = None

Name of an existing UC Connection for stream platform access. Must be the correct type for the streaming platform (e.g. a Kafka Connection for a Kafka Stream).

as_dict() → dict

Serializes the StreamConnectionConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the StreamConnectionConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → StreamConnectionConfig

Deserializes the StreamConnectionConfig from a dictionary.

class databricks.sdk.service.ml.StreamSchemaConfig(direct_schemas: DirectSchemas | None = None)

Schema definitions for the stream. Currently only direct schemas are supported. In a future milestone, we will support schema registries through a UC Connection.

direct_schemas: DirectSchemas | None = None

Schema definitions provided directly on the Stream.

as_dict() → dict

Serializes the StreamSchemaConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the StreamSchemaConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → StreamSchemaConfig

Deserializes the StreamSchemaConfig from a dictionary.

class databricks.sdk.service.ml.StreamSourceConfig(kafka_stream_config: KafkaStreamConfig | None = None)

Source-specific configuration. Determines the streaming platform source.

kafka_stream_config: KafkaStreamConfig | None = None

Configuration for Apache Kafka streams.

as_dict() → dict

Serializes the StreamSourceConfig into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the StreamSourceConfig into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → StreamSourceConfig

Deserializes the StreamSourceConfig from a dictionary.

class databricks.sdk.service.ml.StreamingMode(mode: StreamingModeStreamingModeType | None = None)

The streaming mode configuration for a streaming materialization pipeline.

mode: StreamingModeStreamingModeType | None = None

The type of streaming mode used by the materialization pipeline.

as_dict() → dict

Serializes the StreamingMode into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the StreamingMode into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → StreamingMode

Deserializes the StreamingMode from a dictionary.

class databricks.sdk.service.ml.StreamingModeStreamingModeType

STREAMING_MODE_TYPE_MBM = "STREAMING_MODE_TYPE_MBM"

STREAMING_MODE_TYPE_RTM = "STREAMING_MODE_TYPE_RTM"

class databricks.sdk.service.ml.SubscriptionMode(assign: str | None = None, subscribe: str | None = None, subscribe_pattern: str | None = None)

Deprecated: Use KafkaSubscriptionMode instead.

assign: str | None = None

A JSON string that contains the specific topic-partitions to consume from. For example, for ‘{“topicA”:[0,1],”topicB”:[2,4]}’, topicA’s 0’th and 1st partitions will be consumed from.

subscribe: str | None = None

A comma-separated list of Kafka topics to read from. For example, ‘topicA,topicB,topicC’.

subscribe_pattern: str | None = None

A regular expression matching topics to subscribe to. For example, ‘topic.*’ will subscribe to all topics starting with ‘topic’.

as_dict() → dict

Serializes the SubscriptionMode into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SubscriptionMode into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SubscriptionMode

Deserializes the SubscriptionMode from a dictionary.

class databricks.sdk.service.ml.SumFunction(input: str)

Computes the sum of values.

input: str

The input column from which the sum is computed. For Kafka sources, use dot-prefixed path notation (e.g., “value.amount”). For nested fields, the leaf node name is used. TODO(FS-939): Colon-prefixed notation (e.g., “value:amount”) is supported for backwards compatibility but is deprecated; migrate to dot notation.

as_dict() → dict

Serializes the SumFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the SumFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → SumFunction

Deserializes the SumFunction from a dictionary.

class databricks.sdk.service.ml.TableTrigger

A trigger that fires when the upstream source table changes.

as_dict() → dict

Serializes the TableTrigger into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the TableTrigger into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → TableTrigger

Deserializes the TableTrigger from a dictionary.

class databricks.sdk.service.ml.TestRegistryWebhookResponse(body: 'Optional[str]' = None, status_code: 'Optional[int]' = None)

body: str | None = None

Body of the response from the webhook URL

status_code: int | None = None

Status code returned by the webhook URL

as_dict() → dict

Serializes the TestRegistryWebhookResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the TestRegistryWebhookResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → TestRegistryWebhookResponse

Deserializes the TestRegistryWebhookResponse from a dictionary.

class databricks.sdk.service.ml.TimeWindow(continuous: 'Optional[ContinuousWindow]' = None, rolling: 'Optional[RollingWindow]' = None, sliding: 'Optional[SlidingWindow]' = None, tumbling: 'Optional[TumblingWindow]' = None)

continuous: ContinuousWindow | None = None

rolling: RollingWindow | None = None

sliding: SlidingWindow | None = None

tumbling: TumblingWindow | None = None

as_dict() → dict

Serializes the TimeWindow into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the TimeWindow into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → TimeWindow

Deserializes the TimeWindow from a dictionary.

class databricks.sdk.service.ml.TimeseriesColumn(name: 'str')

name: str

The name of the timeseries column. For Kafka sources, use dot-prefixed path notation to reference fields within the key or value schema (e.g., “value.event_timestamp”). For nested fields, the leaf node name (e.g., “event_timestamp” from “value.event_details.event_timestamp”) is what will be present in materialized tables and expected to match at query time. TODO(FS-939): Colon-prefixed notation (e.g., “value:event_timestamp”) is supported for backwards compatibility but is deprecated; migrate to dot notation.

as_dict() → dict

Serializes the TimeseriesColumn into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the TimeseriesColumn into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → TimeseriesColumn

Deserializes the TimeseriesColumn from a dictionary.

class databricks.sdk.service.ml.TransitionRequest(available_actions: List[ActivityAction] | None = None, comment: str | None = None, creation_timestamp: int | None = None, to_stage: str | None = None, user_id: str | None = None)

For activities, this contains the activity recorded for the action. For comments, this contains the comment details. For transition requests, this contains the transition request details.

available_actions: List[ActivityAction] | None = None

Array of actions on the activity allowed for the current viewer.

comment: str | None = None

User-provided comment associated with the activity, comment, or transition request.

creation_timestamp: int | None = None

Creation time of the object, as a Unix timestamp in milliseconds.

to_stage: str | None = None

Target stage of the transition (if the activity is stage transition related). Valid values are:

• None: The initial stage of a model version.

• Staging: Staging or pre-production stage.

• Production: Production stage.

• Archived: Archived stage.

user_id: str | None = None

The username of the user that created the object.

as_dict() → dict

Serializes the TransitionRequest into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the TransitionRequest into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → TransitionRequest

Deserializes the TransitionRequest from a dictionary.

class databricks.sdk.service.ml.TransitionStageResponse(model_version_databricks: 'Optional[ModelVersionDatabricks]' = None)

model_version_databricks: ModelVersionDatabricks | None = None

Updated model version

as_dict() → dict

Serializes the TransitionStageResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the TransitionStageResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → TransitionStageResponse

Deserializes the TransitionStageResponse from a dictionary.

class databricks.sdk.service.ml.TumblingWindow(window_duration: 'str')

window_duration: str

The duration of each tumbling window (non-overlapping, fixed-duration windows).

as_dict() → dict

Serializes the TumblingWindow into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the TumblingWindow into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → TumblingWindow

Deserializes the TumblingWindow from a dictionary.

class databricks.sdk.service.ml.UpdateCommentResponse(comment: 'Optional[CommentObject]' = None)

comment: CommentObject | None = None

Updated comment object

as_dict() → dict

Serializes the UpdateCommentResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the UpdateCommentResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → UpdateCommentResponse

Deserializes the UpdateCommentResponse from a dictionary.

class databricks.sdk.service.ml.UpdateExperimentResponse

as_dict() → dict

Serializes the UpdateExperimentResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the UpdateExperimentResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → UpdateExperimentResponse

Deserializes the UpdateExperimentResponse from a dictionary.

class databricks.sdk.service.ml.UpdateModelResponse(registered_model: 'Optional[Model]' = None)

registered_model: Model | None = None

as_dict() → dict

Serializes the UpdateModelResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the UpdateModelResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → UpdateModelResponse

Deserializes the UpdateModelResponse from a dictionary.

class databricks.sdk.service.ml.UpdateModelVersionResponse(model_version: 'Optional[ModelVersion]' = None)

model_version: ModelVersion | None = None

Return new version number generated for this model in registry.

as_dict() → dict

Serializes the UpdateModelVersionResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the UpdateModelVersionResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → UpdateModelVersionResponse

Deserializes the UpdateModelVersionResponse from a dictionary.

class databricks.sdk.service.ml.UpdateRunResponse(run_info: 'Optional[RunInfo]' = None)

run_info: RunInfo | None = None

Updated metadata of the run.

as_dict() → dict

Serializes the UpdateRunResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the UpdateRunResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → UpdateRunResponse

Deserializes the UpdateRunResponse from a dictionary.

class databricks.sdk.service.ml.UpdateRunStatus

Status of a run.

FAILED = "FAILED"

FINISHED = "FINISHED"

KILLED = "KILLED"

RUNNING = "RUNNING"

SCHEDULED = "SCHEDULED"

class databricks.sdk.service.ml.UpdateWebhookResponse(webhook: 'Optional[RegistryWebhook]' = None)

webhook: RegistryWebhook | None = None

as_dict() → dict

Serializes the UpdateWebhookResponse into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the UpdateWebhookResponse into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → UpdateWebhookResponse

Deserializes the UpdateWebhookResponse from a dictionary.

class databricks.sdk.service.ml.VarPopFunction(input: str)

Computes the population variance.

input: str

The input column from which the population variance is computed.

as_dict() → dict

Serializes the VarPopFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the VarPopFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → VarPopFunction

Deserializes the VarPopFunction from a dictionary.

class databricks.sdk.service.ml.VarSampFunction(input: str)

Computes the sample variance.

input: str

The input column from which the sample variance is computed.

as_dict() → dict

Serializes the VarSampFunction into a dictionary suitable for use as a JSON request body.

as_shallow_dict() → dict

Serializes the VarSampFunction into a shallow dictionary of its immediate attributes.

classmethod from_dict(d: Dict[str, Any]) → VarSampFunction

Deserializes the VarSampFunction from a dictionary.

class databricks.sdk.service.ml.ViewType

Qualifier for the view type.

ACTIVE_ONLY = "ACTIVE_ONLY"

ALL = "ALL"

DELETED_ONLY = "DELETED_ONLY"