1.30.0

1.30.0

New Features

• Experiment Tracking live logging (PrPr): In SPCS, call set_live_logging_status(True) to automatically capture and
  persist outputs to stdout and stderr while a run is active. The captured logs can be viewed from the Experiments UI.
• Registry: Support logging MLflow models created via mlflow.*.save_model() to the Snowflake Model
  Registry. Previously, only models logged through mlflow.*.log_model() were supported. This also
  enables logging custom mlflow.pyfunc.PythonModel subclasses saved locally.

Bug Fixes

• Registry: Fixed Prophet model handler to correctly mark the predict method as partitioned, ensuring it
  uses a partitioned TABLE_FUNCTION when deployed to Snowflake.
• Registry: Support text-generation models without chat template. The model will have signatures to automatically take
  plain strings as input without needing to specify the signatures in log_model.
  The signature can be overridden if the user chooses to.

Behavior Changes

• Registry: Huggingface models with task text-generation that do not have chat templates will be logged with signature
  that supports plain text (string) as input.

Deprecations

• Registry: Removed support for logging Hugging Face Pipelines in config-only mode. Config-only
  models could not run in warehouse and required an External Access Integration (EAI) with egress
  to Hugging Face hosts. Use remote logging or local download instead — these approaches support
  warehouse execution and store model weights at log time, enabling fully air-gapped services.