v0.142.0
Summary
The Databricks Python SDK introduces skill management operations to the AI Gateway workspace client. Users can now create, retrieve, list, update, finalize, and delete skills programmatically using new methods on the AI Gateway service.
Summary generated by brickster.ai. For the full changelog and any code/binary attachments, follow the GitHub link above.
More from databricks/databricks-sdk-py
The MaterializedFeature class now includes job_id and pipeline_id fields. Importing the SDK runtime, WorkspaceClient, or dbutils no longer configures the root logger, preventing interference with subsequent logging.basicConfig calls.
This release adds support for the new Mason workspace-level service via the w.mason client. It also introduces service_credential and secret_reference fields to Unity Catalog model provider configurations.
The SDK adds new credential management methods for MCP services in AI Gateway and expands job notifications to include maintenance-related events. Support for additional data formats (Avro, Protobuf) in pipelines and new connection types (TikTok Ads, Smartsheet) extend integration capabilities.
This release adds several new optional fields across Jobs, Serving, and Settings APIs, including priority class configuration for AI Runtime tasks, UC service credentials for Amazon Bedrock, and workspace label management. A new gpu_8x_b300 accelerator type is now available for compute specifications.
The v0.138.0 release adds new configuration fields across the Python SDK for AI runtime tasks (unity_catalog_image_path), ML data sources (feature_view_source), and ML publish specifications (budget_policy_id and tags). A development field has also been added to pipeline update operations.
The SDK adds a purge_feature_entities() method for feature engineering and introduces new deny policy type support with a deny field in PolicyInfo. New budget_policy_id and tags fields are available for IngestionConfig and MaterializedFeature to enable budget policy tracking and resource tagging.