Snowpark ML release notes

This article contains the release notes for the Snowpark ML, including the following when applicable:

  • Behavior changes

  • New features

  • Customer-facing bug fixes

Note

These notes do not include changes in features that have not been publicly released.

Version 1.1.2 (2023-12-18)

New features and updates

Model development updates:

  • Implemented precision_score metric in SQL.

Bug fixes

  • Fixed an issue where stack trace was being hidden by telemetry.

Model development fixes:

  • Inferring model signatures no longer materializes the full dataframe in memory.

Version 1.1.1 (2023-12-6)

New features and updates

  • Designated Snowpark ML Modeling API as Generally Available.

  • New passthrough_col parameter in the Modeling API allows you to exclude specific columns, like index columns, during training or inference when not explicitly specifying input_cols.

Bug fixes

  • Model development fixes:

    • confusion_matrix provided incorrect results when the row number could not be divided by the batch size.

Version 1.1.0 (2023-12-1)

New features and updates

  • GridSearchCV and RandomizedSearchCV execution is now distributed on multi-node warehouses.

Bug fixes

  • Model development fixes:

    • Columns were being excluded if their normalized names did not match the names specified in output_columns in OrdinalEncoder and LabelEncoder. Output columns no longer need to be valid Snowflake identifiers.

Version 1.0.12 (2023-11-15)

New features and updates

  • None

Bug fixes

  • Model development fixes:

    • Increased the column capacity of OrdinalEncoder.

Version 1.0.11 (2023-10-28)

New features and updates

  • Add support for kneighbors.

  • Support DecimalType as a data type.

Bug fixes

  • Model development fixes:

    • Fix support for XGBoost and LightGBM models using SKLearn Grid Search and Randomized Search model selectors.

    • Fix metrics compatibility with Snowpark DataFrames that use Snowflake identifiers

Version 1.0.10 (2023-10-15)

New features and updates

  • precision_score, recall_score, f1_score, fbeta_score, precision_recall_fscore_support, mean_absolute_error, mean_squared_error, and mean_absolute_percentage_error metric calculations are now distributed.

Bug fixes

  • Model development fixes:

    • Fix UTF-8 decoding errors when using modeling modules on Windows.

    • Fix alias definitions causing SnowparkSQLUnexpectedAliasException in inference.

Version 1.0.9 (2023-09-28)

New features and updates

  • Calculation of log_loss metric is now distributed.

Bug fixes

  • Model development fixes:

    • Building images no longer fails with some Docker setups.

    • Embedding local ML library no longer fails when the library is imported by zipimport.

    • Update incorrect documentation about platform argument in the deploy function.

Version 1.0.8 (2023-09-15)

New features and updates

  • None

Bug fixes

  • Model development fixes:

    • Ordinal encoder can be used with mixed input column types.

    • Fix an issue when the sklearn default value is np.nan.

Version 1.0.7 (2023-09-05)

New features and updates

  • Allow disabling telemetry.

Bug fixes

  • Model development fixes:

    • Fix an error related to pandas.io.json.json_normalizer.

Version 1.0.6 (2023-09-01)

New features and updates

  • Model development: Size of metrics result can exceed previous 8MB limit.

Bug fixes

  • Model development fixes:

    • Fixed a bug when using simple imputer with NumPy >= 1.25.

    • Fixed a bug when inferring the type of label columns.

Version 1.0.5 (2023-08-17)

This release contains internal changes only.

Version 1.0.4 (2023-07-28)

New features and updates

  • Model development: Input dataframes can now be joined against data loaded from staged files.

  • Model development: Added support for non-English languages.

Bug fixes

  • None

Version 1.0.3 (2023-07-14)

This release contains internal changes only.

Version 1.0.2 (2023-06-22)

New features and updates

  • Model development: Added metrics:

    • d2_absolute_error_score

    • d2_pinball_score

    • explained_variance_score

    • mean_absolute_error

    • mean_absolute_percentage_error

    • mean_squared_error

Bug fixes

Model development: accuracy_score now works when given label column names that are lists of single values.

Version 1.0.1 (2023-06-16)

Behavior changes

  • Model development: Changed Metrics APIs to follow scikit-learn metrics modules:

    • accuracy_score, confusion_matrix, precision_recall_fscore_support, and precision_score methods move to metrics.classification.

New features and updates

  • Model development: Added metrics:

    • f1_score

    • fbeta_score

    • recall_score

    • roc_auc_score

    • roc_curve

    • log_loss

    • precision_recall_curve

Version 1.0.0 (2023-06-09)

Initial public preview release.