added documentation

Author: KshitizLohia

ads/feature_store/docs/source/data_versioning.rst

@@ -3,7 +3,7 @@ Data Versioning
 
 Data versioning is a practice aimed at recording the various data commits integrated into a particular feature group and dataset. This involves tracking changes in data over time while maintaining consistent schema structures and feature definitions within a shared schema version. In the context of feature store, it's important to note that data versioning features are exclusively available for offline feature groups.
 
-.. image:: figures/dataset_versioning.png
+.. image:: figures/data_versioning.png
 
 
 As Of

ads/feature_store/docs/source/feature_validation.rst

@@ -7,8 +7,9 @@ Feature validation is the process of checking the quality and accuracy of the fe
 Feature store allows you to define expectation on the data which is being materialized into feature group and dataset. This is achieved using open source library Great Expectations.
 
 .. note::
-  `Great Expectations <https://docs.greatexpectations.io/docs/0.15.50/>`_ is a Python-based open-source library for validating, documenting, and profiling your data. It helps you to maintain data quality and improve communication about data between teams. Software developers have long known that automated testing is essential for managing complex codebases.
+  `Great Expectations <https://docs.greatexpectations.io/docs/0.15.50/>`_ is a Python-based open-source library for validating, documenting, and profiling your data. It helps you to maintain data quality and improve communication about data between teams. Software developers have long known that automated testing is essential for managing complex codebases. Great Expectations empowers you to define and enforce your data expectations when handling and processing data, allowing for swift detection of data anomalies. In essence, Expectations serve as the equivalent of unit tests for your data, enabling you to rapidly identify and address data-related problems. Beyond this, Great Expectations offers the added benefit of generating comprehensive data documentation and quality reports based on these Expectations.
 
+.. image:: figures/data_validation.png
 
 Expectations
 ============

ads/feature_store/docs/source/figures/data_validation.png

@@ -49,3 +49,10 @@ The statistical metrics that are computed by feature store depend on the feature
 +------------------------+-----------------------+
 | Kurtosis               |                       |
 +------------------------+-----------------------+
+
+Drift Monitoring
+================
+
+Models can fail silently. Over and over we see the root cause of model issues in production can be traced back to the data itself, not the model. By applying data monitoring to the feature store, practitioners can automatically catch data issues like missing values, change in data format or unexpected values (change in data cardinality), and data drift upstream before the models are impacted
+
+.. image:: figures/drift_monitoring.png