2.1.0

2.1.0

New Features

• Experiment Tracking: Added ExperimentTracking.get_metric_history, which returns every logged step of a
  metric instead of only the value at the highest step reported by list_metrics. Pass a metric name to scope
  the result to one metric, or omit it to get every metric of the run. The result is a lazy Snowpark DataFrame
  with name, step, value, and timestamp columns, so filtering and aggregation run in Snowflake.

• Feature Store: list_feature_views() now surfaces the online feature table's setup readiness in the
  online_config JSON as setup_status, setup_error_msg and setup_time, for both batch/streaming
  and realtime feature views. The keys are present only when setup-readiness information is available
  for the table, so test for key presence. SETUP_NOTREADY means setup has not concluded yet, not
  that setup failed; SETUP_FAILED is the failure signal. Note that SETUP_READY is also reported
  when nothing has ever been reported for the table, and in that case setup_status may later change
  to SETUP_FAILED.

Bug Fixes

• Registry: Fixed logging of MLflow models created with mlflow.sklearn.save_model(). The model is
  now re-logged from the scikit-learn estimator using the serialization format it was saved with,
  instead of from MLflow's PyFunc wrapper with the version-dependent default format.

Behavior Changes

Deprecations