v0.136.0
Summary
The SDK adds a new `domains` workspace-level service for managing domains. ML Feature Store capabilities are expanded with new fields for feature filtering, scheduling, and improved source column specifications across Delta tables and Kafka sources.
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 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.
The SDK adds an execute_command_sync() method for sandbox and new fields for ML features (mode, time_window, full_feature_name). Breaking changes remove routing and rate limiting configuration fields from model serving APIs.
The Pipelines API's source_table and source_schema fields are now optional to support streaming and message-bus connectors that don't use source tables or schemas. New ai_functions and sandbox workspace services are available, along with expanded APIs for bundle deployments, feature engineering, PostgreSQL, IAM v2, and model deployment resources.