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+# Release History
+
+## 1.0.1 (2023-06-16)
+### 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 (2023-06-09)
+
+### Behavior Changes
+
+- 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 model in the warehouse.
+- Model Registry: Removed `id` field from `ModelReference` constructor.
+- 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.
+
+### New Features
+
+- Added PolynomialFeatures transformer to the 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 Deployment: `deploy()` & `predict()` methods now correctly escapes identifiers
+
+## 0.3.2 (2023-05-23)
+
+### Behavior Changes
+
+- Use cloudpickle to serialize and deserialize models throughout the codebase and removed dependency on joblib.
+
+### New Features
+
+- Model Deployment: Added support for snowflake.ml models.
+
+## 0.3.1 (2023-05-18)
+
+### Behavior Changes
+
+- Standardized registry API with following
+  - Create & open registry taking same set of arguments
+  - Create & Open can choose schema to use
+  - Set_tag, set_metric, etc now explicitly calls out arg name as metric_name, tag_name, metric_name, etc.
+
+### New Features
+
+- Changes to support python 3.9, 3.10
+- Added kBinsDiscretizer
+- Support for deployment of XGBoost models & int8 types of data
+
+## 0.3.0 (2023-05-11)
+
+### Behavior Changes
+
+- Big Model Registry Refresh
+  - Fixed API discrepancies between register_model & log_model.
+  - Model can be referred by Name + Version (no opaque internal id is required)
+
+### New Features
+
+- Model Registry: Added support save/load/deploy SKL & XGB Models
+
+## 0.2.3 (2023-04-27)
+
+### Bug Fixes
+
+- Allow using OneHotEncoder along with sklearn style estimators in a pipeline.
+
+### New Features
+
+- Model Registry: Added support for delete_model. Use delete_artifact = False to not delete the underlying model data but just unregister.
+
+## 0.2.2 (2023-04-11)
+
+### New Features
+
+- Initial version of snowflake-ml modeling package.
+  - Provide support for training most of scikit-learn and xgboost estimators and transformers.
+
+### Bug Fixes
+
+- Minor fixes in preprocessing package.
+
+## 0.2.1 (2023-03-23)
+
+### New Features
+
+- New in Preprocessing:
+  - SimpleImputer
+  - Covariance Matrix
+- Optimization of Ordinal Encoder client computations.
+
+### Bug Fixes
+
+- Minor fixes in OneHotEncoder.
+
+## 0.2.0 (2023-02-27)
+
+### New Features
+
+- Model Registry
+- PyTorch & Tensorflow connector file generic FileSet API
+- New to Preprocessing:
+  - Binarizer
+  - Normalizer
+  - Pearson correlation Matrix
+- Optimization in Ordinal Encoder to cache vocabulary in temp tables.
+
+## 0.1.3 (2023-02-02)
+
+### New Features
+
+- Initial version of transformers including:
+  - Label Encoder
+  - Max Abs Scaler
+  - Min Max Scaler
+  - One Hot Encoder
+  - Ordinal Encoder
+  - Robust Scaler
+  - Standard Scaler