Snowflake-ml-python

New Features

- FileSet: `snowflake.ml.fileset.sfcfs.SFFileSystem` can now be used in UDFs and stored procedures.

Bug Fixes

- Registry: Fix a bug that leads to module in `code_paths` when `log_model` cannot be correctly imported.
- Registry: Fix incorrect error message when validating input Snowpark DataFrame with array feature.
- Model Registry: Fix an issue when deploying a model to SPCS that some files do not have proper permission.
- Model Development: Relax package versions for all inference methods if the installed version
is not available in the Snowflake conda channel

Behavior Changes

- Registry: When running the method of a model, the value range based input validation to avoid input from overflowing
is now optional rather than enforced, this should improve the performance and should not lead to problem for most
kinds of model. If you want to enable this check as previous, specify `strict_input_validation=True` when
calling `run`.
- Registry: By default `relax_version=True` when logging a model instead of using the specific local dependency versions.
This improves dependency versioning by using versions available in Snowflake. To switch back to the previous behavior
and use specific local dependency versions, specify `relax_version=False` when calling `log_model`.
- Model Development: The behavior of `fit_predict` for all estimators is changed.
Firstly, it will cover all the estimator that contains this function,
secondly, the output would be the union of pandas DataFrame and snowpark DataFrame.

New Features

- FileSet: `snowflake.ml.fileset.sfcfs.SFFileSystem` can now be serialized with `pickle`.

Bug Fixes

- Registry: Now when providing Decimal Type column to a DOUBLE or FLOAT feature will not error out but auto cast with
warnings.
- Registry: Improve the error message when specifying currently unsupported `pip_requirements` argument.
- Model Development: Fix precision_recall_fscore_support incorrect results when `average="samples"`.
- Model Registry: Fix an issue that leads to description, metrics or tags are not correctly returned in newly created
Model Registry (PrPr) due to Snowflake BCR [2024_01](https://docs.snowflake.com/en/release-notes/bcr-bundles/2024_01/bcr-1483)

Behavior Changes

- Feature Store: `FeatureStore.suspend_feature_view` and `FeatureStore.resume_feature_view` doesn't mutate input feature
view argument any more. The updated status only reflected in the returned feature view object.

New Features

- Model Development: support `score_samples` method for all the classes, including Pipeline,
GridSearchCV, RandomizedSearchCV, PCA, IsolationForest, ...
- Registry: Support deleting a version of a model.

New Features

- Model Registry: Support providing external access integrations when deploying a model to SPCS. This will help and be
required to make sure the deploying process work as long as SPCS will by default deny all network connections. The
following endpoints must be allowed to make deployment work: docker.com:80, docker.com:443, anaconda.com:80,
anaconda.com:443, anaconda.org:80, anaconda.org:443, pypi.org:80, pypi.org:443. If you are using
`snowflake.ml.model.models.huggingface_pipeline.HuggingFacePipelineModel` object, the following endpoints are required
to be allowed: huggingface.com:80, huggingface.com:443, huggingface.co:80, huggingface.co:443.

New Features

- Model Development: Infers output column data type for transformers when possible.
- Registry: `relax_version` option is available in the `options` argument when logging the model.

Bug Fixes

- Model Registry: Fix "XGBoost version not compiled with GPU support" error when running CPU inference against open-source
XGBoost models deployed to SPCS.
- Model Registry: Fix model deployment to SPCS on Windows machines.

New Features

- Model Development: Introduced XGBoost external memory training feature. This feature enables training XGBoost models
on large datasets that don't fit into memory.
- Registry: New Registry class named `snowflake.ml.registry.Registry` providing similar APIs as the old one but works
with new MODEL object in Snowflake SQL. Also, we are providing`snowflake.ml.model.Model` and
`snowflake.ml.model.ModelVersion` to represent a model and a specific version of a model.
- Model Development: Add support for `fit_predict` method in `AgglomerativeClustering`, `DBSCAN`, and `OPTICS` classes;
- Model Development: Add support for `fit_transform` method in `MDS`, `SpectralEmbedding` and `TSNE` class.

Additional Notes

- Model Registry: The `snowflake.ml.registry.model_registry.ModelRegistry` has been deprecated starting from version
1.2.0. It will stay in the Private Preview phase. For future implementations, kindly utilize
`snowflake.ml.registry.Registry`, except when specifically required. The old model registry will be removed once all
its primary functionalities are fully integrated into the new registry.