Metadata-Version: 2.1
Name: databricks-feature-engineering
Author: Databricks
Author-email: feedback@databricks.com
License: Databricks Proprietary License
Description-Content-Type: text/markdown
Summary: Databricks Feature Engineering Client
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Requires-Dist: mlflow-skinny[databricks]>=2.16.0, <4
Requires-Dist: pyyaml>=6, <7
Requires-Dist: boto3>=1.16.7, <2
Requires-Dist: dbl-tempo>=0.1.26, <1
Requires-Dist: azure-cosmos==4.3.1
Requires-Dist: numpy>=1.19.2, <3
Requires-Dist: protobuf>=5, <7
Requires-Dist: sqlparse<1, >=0.4.0
Requires-Dist: databricks-sdk>=0.157.0, <1
Version: 0.18.3

The Databricks Feature Engineering client is used to:
* Create, read, and write feature tables
* Train models on feature data
* Publish feature tables to online stores for real-time serving
* Upgrade workspace feature table metadata to Unity Catalog

# Documentation

Documentation can be found per-cloud at:
- [AWS](https://docs.databricks.com/applications/machine-learning/feature-store/index.html)
- [Azure](https://docs.microsoft.com/en-us/azure/databricks/applications/machine-learning/feature-store/)
- [GCP](https://docs.gcp.databricks.com/applications/machine-learning/feature-store/index.html)

For release notes, see
- [AWS](https://docs.databricks.com/release-notes/feature-store/databricks-feature-store.html)
- [Azure](https://docs.microsoft.com/en-us/azure/databricks/release-notes/feature-store/databricks-feature-store)
- [GCP](https://docs.gcp.databricks.com/release-notes/feature-store/databricks-feature-store.html)

# Limitations

This library can only run on Databricks runtimes. It can be installed locally or in CI/CD environments for unit testing.

