1.31.0

1.31.0

New Features

Bug Fixes

• Registry: Fixed custom model handler to explicitly persist PARTITIONED=False in model metadata for
  @inference_api methods. Previously, custom models using @inference_api with TABLE_FUNCTION type
  were incorrectly reported as partitioned.
• Registry: Fixed sklearn and SnowML model handlers to explicitly persist PARTITIONED=False metadata for
  the explain method, preventing it from being incorrectly reported as partitioned at read time.
• Registry: Fixed inconsistent infer_signature for columns containing NaN values. Previously,
  the inferred dtype (DOUBLE vs INT64) depended on whether NaN rows survived truncation, causing
  downstream validation failures. Now, the original pandas column dtype is respected during infer_signature,
  so columns with mix of integer and np.nan or None are consistently inferred as DOUBLE regardless of NaN position.
• Registry: Fixed a bug where invalid parameter types (e.g., passing a string to an integer parameter)
  were silently coerced instead of raising an error. Parameters are now strictly validated against their
  declared types before inference. This may break code that relied on the silent coercion behavior
  (e.g., model.run(max_tokens="100") or model.run(some_int_param=True)).
• ML Job: Fixed explicitly named MLJobDefinition objects so they can be overwritten in place and invoked
  repeatedly without job name collisions; the generate_suffix argument has been removed.
• Experiment Tracking live logging (PrPr): Fixed a bug that generated too many lines of log that consist entirely of
  whitespace.

Behavior Changes

• Registry: Warehouse partitioned model inference no longer includes non-partition input columns (which were always
  NULL) in the output. The output now contains only the model's output columns and the partition column.

Deprecations