[1.0.4] Release

1.0.4

New Features

• Model Registry: Added support save/load/deploy Tensorflow models (tensorflow.Module).
• Model Registry: Added support save/load/deploy MLFlow PyFunc models (mlflow.pyfunc.PyFuncModel).
• Model Development: Input dataframes can now be joined against data loaded from staged files.
• Model Development: Added support for non-English languages.

Bug Fixes

• Model Registry: Fix an issue that model dependencies are incorrectly reported as unresolvable on certain platforms.

[1.0.3] Release

1.0.3 (2023-07-14)

Behavior Changes

• Model Registry: When predicting a model whose output is a list of NumPy ndarray, the output would not be flattened, instead, every ndarray will act as a feature(column) in the output.

New Features

• Model Registry: Added support save/load/deploy PyTorch models (torch.nn.Module and torch.jit.ScriptModule).

Bug Fixes

• Model Registry: Fix an issue that when database or schema name provided to create_model_registry contains special characters, the model registry cannot be created.
• Model Registry: Fix an issue that get_model_description returns with additional quotes.
• Model Registry: Fix incorrect error message when attempting to remove a unset tag of a model.
• Model Registry: Fix a typo in the default deployment table name.
• Model Registry: Snowpark dataframe for sample input or input for predict method that contains a column with Snowflake NUMBER(precision, scale) data type where scale = 0 will not lead to error, and will now correctly recognized as INT64 data type in model signature.
• Model Registry: Fix an issue that prevent model logged in the system whose default encoding is not UTF-8 compatible from deploying.
• Model Registry: Added earlier and better error message when any file name in the model or the file name of model itself contains characters that are unable to be encoded using ASCII. It is currently not supported to deploy such a model.

[1.0.2] Release

1.0.2 (2023-06-22)

Behavior Changes

• Model Registry: Prohibit non-snowflake-native models from being logged.
• Model Registry: _use_local_snowml parameter in options of deploy() has been removed.
• Model Registry: A default False embed_local_ml_library parameter has been added to the options of log_model(). With this set to False (default), the version of the local snowflake-ml-python library will be recorded and used when deploying the model. With this set to True, local snowflake-ml-python library will be embedded into the logged model, and will be used when you load or deploy the model.

New Features

• Model Registry: A new optional argument named code_paths has been added to the arguments of log_model() for users to specify additional code paths to be imported when loading and deploying the model.
• Model Registry: A new optional argument named options has been added to the arguments of log_model() to specify any additional options when saving the model.
• 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 are lists of a single value.

[1.0.1]. Release

Behavior Changes

• Model Development: Changed Metrics APIs to imitate sklearn metrics modules:
  • accuracy_score(), confusion_matrix(), precision_recall_fscore_support(), precision_score() methods move from respective modules to metrics.classification.
• Model Registry: The dafault table/stage created by the Registry now uses "SYSTEM" as a prefix.
• Model Registry: get_model_history() method as been enhanced to include the history of model deployment.

New Features

• Model Registry: A default False flag named replace_udf has been added to the options of deploy(). Setting this to True will allow overwrite existing UDF with the same name when deploying.
• Model Development: Added metrics:
  • f1_score
  • fbeta_score
  • recall_score
  • roc_auc_score
  • roc_curve
  • log_loss
  • precision_recall_curve
• Model Registry: A new argument named permanent has been added to the arguemnt of deploy(). Setting this to True allows the creation of a permanent deployment without needing to specify the UDF location.
• Model Registry: A new method list_deployments() has been added to enumerate all permanent deployments originating from a specific model.
• Model Registry: A new method get_deployment() has been added to fetch a deployment by its deployment name.
• Model Registry: A new method delete_deployment() has been added to remove an existing permanent deployment.

[1.0.0]. Release

Behavior Changes

• Model Development: Preprocessing and Metrics move to the modeling package: snowflake.ml.modeling.preprocessing and snowflake.ml.modeling.metrics.
• Model Development: get_sklearn_object() method is renamed to to_sklearn(), to_xgboost(), and to_lightgbm() for respective native models.
• Model Registry: predict() method moves from Registry to ModelReference.
• Model Registry: _snowml_wheel_path parameter in options of deploy(), is replaced with _use_local_snowml with default value of False. Setting this to True will have the same effect of uploading local SnowML code when executing a model in the warehouse.
• Model Registry: Removed id field from ModelReference constructor.

New Features

• Added PolynomialFeatures transformer to snowflake.ml.modeling.preprocessing module.
• Added metrics:
  • accuracy_score
  • confusion_matrix
  • precision_recall_fscore_support
  • precision_score

Bug Fixes

• Model Registry: Model version can now be any string (not required to be a valid identifier)
• Model Development: deploy() & predict() methods now correctly escapes identifiers

[0.3.3] Release

Project import generated by Copybara. (#18)

GitOrigin-RevId: 288c0c4da10ce230b81b6eb80316011cbb76252b

Co-authored-by: Snowflake Authors <[email protected]>

[0.3.2] Release

• Use cloudpickle to serialize and deserialize models between user workspace and sproc.
• Removed dependency on joblib.
• Modified default path in registry to also use cloudpickle for serialization and deserialization of models.

[0.3.1] Release

1. Adopt to the behavior change release to support python 3.9, 3.10
2. Add KbinsDiscretizer to release
3. Registry Standardlize registry APIs

[0.3.0] Model Registry Refresh

1. Save and deploy sklearn and xgboost models in model registry.
2. Model can be referenced using model name and version as opposed to opaque id.
3. Bug fixes in modeling and telemetry.