Snowflake ML release notes¶

New features¶

New model registry features:

• Model registry now supports asynchronous model inference service creation with the block option in the Modelversion.create__service method. Set this option to False to create the service asynchronously. The default is True.

Bug fixes¶

Model explainability bug fixes:

• Fixed issue where explain is enabled for scikit-learn pipelines whose task is UNKNOWN, only to later fail when invoked.

New features¶

New model registry features:

• Null values are now ignored in the dataframe used for model signature inference. Only non-null values are used to infer signatures.

• Null values are now allowed in dataframes used for prediction.

• pandas extension data types are now supported in model signature inference.

• pandas Series can be used in input and output data.

New model monitoring features:

• The option enable_monitoring is now available when logging a model in the registry. This option gates access to private preview features of model monitoring.

Bug fixes¶

Data bug fixes:

• Missing snowflake.ml.data exports in wheel have been added.

Dataset bug fixes:

• Missing snowflake.ml.dataset exports in wheel have been added.

Model registry bug fixes:

• Fixed issue where tf_keras.Model was not recognized as a keras model when logging.

Behavior changes¶

General behavior changes:

• Python 3.9 is now the minimum required version.

Data connector behavior changes:

• to_torch_dataset and to_torch_datapipe now create a dimension of 1 for scalar data. This allows more seamless integration with the PyTorch DataLoader, which creates batches by stacking inputs. The following example illustrates the difference.

  ds = connector.to_torch_dataset(shuffle=False, batch_size=3)
  

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  • Input data: "col1": [10, 11, 12]

    • Previous result: array([10., 11., 12.]) with shape (3,)

    • New result: array([[10.], [11.], [12.]]) with shape (3, 1)

  • Input data: [[0, 100], [1, 110], [2, 200]]

    • Previous result: array([[ 0, 100], [ 1, 110], [ 2, 200]]) with shape (3,2)

    • New result: No change

• You can now specify a batch size of None in to_torch_dataset to squeeze dimensions of 1 for better interoperability with the PyTorch DataLoader. ::code::None is the new default batch size.

Model Development behavior changes:

• The eps (epsilon) argument is no longer used with the log_loss metric. The argument is still accepted for backward compatibility, but its value is ignored, and the epsilon is now computed by the underlying scikit-lean implementation.

Model Registry behavior changes:

• External access integrations are no longer required when creating an inference service in Snowflake 8.40 or later.

New featres¶

New Model Registry features:

• You can now pass keyword arguments when instantiating ModelContext to provide a variable number of context values. For example:

  mc = custom_model.ModelContext(
      config = 'local_model_dir/config.json',
      m1 = model1
  )
  
  class ExamplePipelineModel(custom_model.CustomModel):
      def __init__(self, context: custom_model.ModelContext) -> None:
          super().__init__(context)
          v = open(self.context['config']).read()
          self.bias = json.loads(v)['bias']
      @custom_model.inference_api
      def predict(self, input: pd.DataFrame) -> pd.DataFrame:
          model_output = self.context['m1'].predict(input)
          return pd.DataFrame({'output': model_output + self.bias})
  

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• Support for pandas’s CategoricalDtype for categorical columns.

• log_model method now accepts both signature and sample_input_data parameters to capture background data from explainablity and data lineage.

Bug fixes¶

Data Connector bug fixes:

• For multi-dimensional data, to_torch_dataset and to_torch_datapipe now return a numpy array with an appropriate data type instead of a list.

Feature Store bug fixes:

• Fixed an issue where ExampleHelper used an incomplete table name.

• Changed weather features aggregation time to one hour instead of one day.

Model Explainability bug fixes:

• Fixed an issue with explainability for XGBoost models by using a new SHAP library version.

Bug fixes¶

Model Registry bug fixes:

• Fix issue with using ModelVersion.run with Model Serving (inference on SPCS).

Behavior changes¶

Model Registry behavior changes:

• This release no longer contains the preview Model Registry API. Use the public API in snowflake.ml.model_registry instead.

Bug fixes¶

Model Registry bug fixes:

• Fix unexpected package name normaliations for packages that do not follow PEP-508 conventions when logging a model.

• Fix “Not a valid remote URI” error when logging MLflow models.

• Fix nested calls to ModelVersion.run.

• Fix log_model failure when a local package version number contains parts other than the base version.

New features¶

New Model Registry features:

• You can now set a task type for the model in log_model via the task parameter.

New Feature Store features:

• FeatureView now supports ON_CREATE and ON_SCHEDULE initializion modes.

Bug fixes¶

• Fix a bug involving invalid names passed where fully-qualified names were required. These now correctly raise an exception.

Modeling bug fixes:

• Correctly log models built using XGBoost version 2 and higher.

Model explainability bug fixes:

• Workarounds and better error handling for XGBoost version 2.1.0 and higher.

• Correctly handle multiclass XGBoost classification models

New features¶

New Feature Store features:

• The update_feature_view method now accepts a FeatureView object as an alternative to name and version.

Bug fixes¶

Feature Store bug fixes:

• Metadata size is no longer limited when generating a dataset.

Model Registry bug fixes:

• Fix an error message in the run method of model versions when a function name is not given and the model has multiple target methods.

New features¶

New Modeling features:

• The set_params method is now available to set the parameters of the underlying scikit-learn estimator, if the Snowpark ML model has been fitted.

New Model Registry features:

• Support for model explainability in XGBoost, LightGBM, CatBoost, and scikit-learn models supported by the shap ibrary.

Behavior changes¶

Feature Store behavior changes:

• Many positional arguments are now keyword arguments. The following table lists the affected arguments for each method.

  Method	Arguments
  Entity initializer	desc
  FeatureView initializer	timestamp_col, refresh_freq, desc
  FeatureStore initializer	creation_mode
  FeatureStore.update_entity	desc
  FeatureStore.register_feature_view	block, overwrite
  FeatureStore.list_feature_views	entity_name, feature_view_name
  FeatureStore.get_refresh_history	verbose
  Feature:Store.retrieve_feature_values	spine_timestamp_col, exclude_columns, include_feature_view_timestamp_col
  FeatureStore.generate_training_set	save_as, spine_timestamp_col, spine_label_cols, exclude_columns, include_feature_view_timestamp_col
  FeatureStore.generate_dataset	version, spine_timestamp_col, spine_label_cols, exclude_columns, include_feature_view_timestamp_col, desc, output_type

• Add new column warehouse to the output of list_feature_views.

Bug fixes¶

Modeling bug fixes:

• Fixed an issue in which SimpleImputer could not impute integer columns with integer values.

Model Registry bug fixes:

• Fixed an issue when providing a non-zero-index-based pandas Dataframe ModelVersion.run.

New features¶

New Feature Store features:

• Added overloads to certain methods to accept both a FeatureView and name/version strings. Affected APIs include read_feature_view, refresh_feature_view, get_refresh_history, resume_feature_view, suspend_feature_view, and delete_feature_view.

• Added docstring inline examples for all public APIs.

• Added ExampleHelper utility class to help with loading source data to simplify public notebooks.

• Added update_entity method.

• Added warehouse argument to FeatureView constructor to override the default warehouse.

New Model Registry features:

• Added option to enable explainability when registering XGBoost, LightGBM, and Catboost models.

• Added support for logging a model from a ModelVersion object.

New modeling features:

• You can disable the 10GB training data size limit in distributed hyperparameter optimization by executing:

  from snowflake.ml.modeling._internal.snowpark_implementations import ( distributed_hpo_trainer, )
  distributed_hpo_trainer.ENABLE_EFFICIENT_MEMORY_USAGE = False
  

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Bug fixes¶

Model Registry bug fixes:

• Fixed “401 Unauthorized” issue when deploying a model to Snowpark Container Services.

Feature Store bug fixes:

• Some exceptions in property setters have been downgraded to warnings, allowing you to change desc, refresh_freq, and warehouse in “draft” feature views.

Modeling bug fixes:

• Fixed issues with calling OneHotEncoder and OrdinalEncoder with a dictionary as the categories parameter and the data in a pandas DataFrame.

New features¶

New Model Registry features:

• Allow overriding device_map and device when loading Hugging Face pipeline models.

• Add set_alias and unset_alias methods to ModelVersion instances to manage the model version’s aliases.

• Add partitioned_inference_api decorator to create partitioned inference methods in models.

New Feature Store features:

• New refresh_freq, refresh_mode, and scheduling_state columns have been added to the output of the list_feature_views method.

• The update_feature_view method now supports updating a feature view’s description.

• New methods refresh_feature_view and get_refresh_history manage updates of feature views.

• New method generate_training_set generates table-backed feature snapshots. generate_dataset(..., output_type="table") has been deprecated and generates a DeprecationWarning.

New Modeling features:

• OneHotEncoder and OrdinalEncoder now accept a list of array-like values for the categories argument.

Bug fixes¶

Model Registry bug fixes:

• Fix an issue causing incorrect results when using a pandas Dataframe with over 100,000 rows as the input of ModelVersion.run method in Stored Procedures.

Modeling bug fixes:

• Fix an issue with passing categories to OneHotEncoder and OrdinalEncoder as a dictionary or as a pandas DataFrame.

New features¶

New Model Registry features:

• Model Registry now supports timestamp (TIMESTAMP_NTZ) columns in input and output data.

New modeling features:

• OneHotEncoder and OrdinalEncoder now support a list of array-like values for the categories argument.

New Dataset features:

• DatasetVersion instances now have label_cols and exclude_cols properties.

Bug fixes¶

Model Registry bug fixes:

• Fixed an issue that prevented calls to log_model in a stored procedure.

Modeling bug fixes:

• Quick fix for import snowflake.ml.modeling.parameters.enable_anonymous_sproc not working due to package dependency error.

New features¶

New Model Registry features:

• log_model, get_model, and delete_model methods now support fully-qualified names.

New modeling features:

• You can now use an anonymous stored procedure during fitting, so that modeling does not require privileges to operate on the registry schema. Call import snowflake.ml.modeling.parameters.enable_anonymous_sproc to enable this feature.

Bug fixes¶

Model registry bug fixes:

• Fix issue with loading older models.

Behavior changes¶

Model Registry behavior changes:

• The fit_transform method can now return either a Snowpark DataFrame or a pandas DataFrame, matching the kind of DataFrame passed to the method.

New features¶

New Model Registry features:

• Added support for exporting models from the registry (ModelVersion.export).

• Added support for loading the underlying model object (ModelVersion.load).

• Added support for renaming models (Model.rename).

Bug fixes¶

Model Registry bug fixes:

• Fixed the “invalid parameter SHOW_MODEL_DETAILS_IN_SHOW_VERSIONS_IN_MODEL” error.

New features¶

New Model Registry features:

• Added support for catboost models (catboost.CatBoostClassifier, catboost.CatBoostRegressor).

• Added support for lightgbm models (lightgbm.Booster, lightgbm.LightGBMClassifier, lightgbm.LightGBMRegressor).

Bug fixes¶

Model Registry bug fixes:

• Fixed bug that caused relax_version option to not work.

Behavior changes¶

Model Registry behavior changes:

• The apply method is no longer included as a target method by default when logging an XGBoost model. If you need this method available in logged models, included it manually in the target-methods option:

  log_model(..., options={"target_methods": ["apply", ...]})
  

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New features¶

New model registry features:

• The registry now supports logging sentence transformer models (sentence_transformers.SentenceTransformer).

• The version_name argument is no longer required when logging a model. A random human-readable ID is generated if none is provided.

Bug fixes¶

Model registry bug fixes:

• Fix issue where, when multiple models are called in the same query, models after the first returned incorrect results. This fix is applied when models are logged and does not benefit existing models; you must log your models again to correct this behavior.

Modeling bug fixes:

• Fix bug in registering a model where only methods mentioned in save_model were added to the model signature for Snowpark ML models.

• Fix bug in batch inference methods such as such as predict and predict_log_probe where, when n_jobs was not 1, the methods would not be executed.

• Fix bug in batch inference methods where they could not infer datatypes when the first row of data contained NULL.

• The output column names from distributed hyperparameter optimization are now correctly matched with the Snowflake identifier.

• Relaxed the versions of dependencies of distributed hyperparameter optimization methods; these were too strict and caused these methods to fail.

• scikit-learn is now listed as a dependency of the LightGBM package.

New features¶

FileSet/FileSystem updates:

• snowflake.ml.fileset.sfcfs.SFFileSystem can now be used in UDFs and stored procedures.

Behavior changes¶

Model registry behavior changes:

• As previously announced, the default for the relax_version option (in the options argument of log_model) is now True, allowing more reliable deployment in most cases by permitting dependency versions available in Snowflake.

• When running model methods, value range based input validation (which prevents input from overflowing) is now optional. This should improve performance and should not lead to issues for most types of models. To enable validation, pass the named argument strict_input_validation=True when calling the model’s run method.

Model development behavior changes:

• The fit_predict method now returns either a pandas or a Snowpark DataFrame, depending on the type of the input data, and is available on all classes where it is available in the underlying scikit-learn, xgboost, or lightgbm class.

New features and updates¶

FileSet/FileSystem updates:

• Instances of snowflake.ml.fileset.sfcfs.SFFileSystem can now be serialized with pickle.

Bug fixes¶

Model registry bug fixes:

• Fix a problem with importing log_model in some circumstances.

• Fix an incorrect error message when validating input Snowpark DataFrame with an array feature.

Model development bug fixes:

• Relax package versions for all inference methods when the installed version of a dependency is not available in the Snowflake conda channel.

New features and updates¶

Model development updates:

• All modeling classes now include a score_samples method to calculate the log-likelihood of the given samples.

Model registry updates:

• Decimal type features are automatically cast (with a warning) to a DOUBLE or FLOAT instead of producing an error.

• Improve error message for currently-unsupported pip-requirements option.

• You can now delete a version of a model.

Bug fixes¶

Model development fixes:

• precision_recall_fscore_support returned incorrect results with average="samples".

Model registry fixes:

• Descriptions, models, and tags were not retrieved correctly in newly-created registries under the private preview model registry API due to a recent Snowflake behavior change.

New features and updates¶

Model registry updates:

• You can now specify external access integrations when deploying a model to Snowpark Container Services using the private preview registry API, allowing models to access the internet to retrieve dependencies during deployment. The following endpoints are required for all deployments:

  • docker.com:80

  • docker.com:443

  • anaconda.com:80

  • anaconda.com:443

  • anaconda.org:80

  • anaconda.org:443

  • pypi.org:80

  • pypi.org:443

  For models derived from HuggingFacePipeLineModel, the following endpoints are required.

  • huggingface.com:80

  • huggingface.com:443

  • huggingface.co:80

  • huggingface.co:443

New features and updates¶

Model development updates:

• Infer column data type for transformers when possible.

Model registry updates:

• relax_version option (in options argument of log_model) relaxes dependencies of stated versions to allow newer minor versions when set to True.

New features and updates¶

Public preview release of model registry. See Snowflake Model Registry. The previous private preview release of the model registry has been deprecated, but will continue to be supported while it includes features not yet available in the public preview version.

Model development updates:

• Added support for fit_predict method in AgglomerativeClustering, DBSCAN, and OPTICS classes.

• Added support for fit_transform method in MDS, SpectralEmbedding and TSNE class.