Skip to main content

Azure Machine Learning Feature Store Python SDK

The azureml-featurestore package is the core SDK interface for Azure ML Feature Store. This SDK works along the azure-ai-ml SDK to provide the managed feature store experience.

Main features in the azureml-featurestore package

  • Develop feature set specification in Spark with the ability for feature transformation.
  • List and get feature sets defined in Azure ML Feature Store.
  • Generate and resolve feature retrieval specification.
  • Run offline feature retrieval with point-in-time join.

Getting started

You can install the package via pip install azureml-featurestore

To learn more about Azure ML managed feature store visit https://aka.ms/featurestore-get-started

Change Log

1.2.1 (2025.09.17)

  • Fix bugs

1.2.0 (2025.08.18)

  • Improve online featurestore

1.1.2 (2025.03.27)

  • Fix bugs

1.1.1 (2025.02.25)

  • Fix logging issue

1.1.0 (2024.03.12)

New Features:

  • [Public Preview] Support for DSL (Domain Specific Language) for feature definition. The DSL is a simplified way to define feature set transformations using a declarative syntax.
    • DSL feature set supports custom source
    • DSL feature set supports temporal join lookback, source delay and source lookback
    • DSL feature set supports load from materialized store
    • get_offline_features supports feature sets with different transformations(dsl, udf or none).

1.0.1 (2023.12.28)

  • Update dependencies

1.0.0 (2023.11.14)

  • [GA] Custom feature source: Custom feature source supports customized source process code script with a user defined dictionary as input.
  • [GA] International regions and sovereign cloud support.
  • [GA] Offline backfill materialization now replaces all data within a feature window instead of doing upsert based on timestamp.
  • [GA] Added bootstrap option for materialization, which enables materializing data from offline store into online store.
  • Re-enabling materialization in a feature set now invalidates all previously materialized data.
  • Feature set spec dump now accepts a file path or a folder path as dump target, and an overwrite option to control whether to override the target.
  • Various bug fixes

0.1.0b6 (2023.11.1)

  • Various bug fixes

0.1.0b5 (2023.10.4)

  • Various bug fixes

0.1.0b4 (2023.08.28)

New Features:

  • [Public preview] Added custom feature source. Custom feature source supports customized source process code script with a user defined dictionary as input.

  • [Public preview] Added csv feature source, deltatable feature source, mltable feature source, parquet feature source as new feature source experience. Previous feature source usage compatibility will be deprecated in 6 months.

  • Bug fixes

Breaking changes:

  • Moved init_online_lookup, shutdown_online_lookup and get_online_features out of FeatureStoreClient, and into the module as standalone functions.
  • get_online_features contract changed from accepting (for the observation_data argument) and returning pandas.DataFrame to accepting (as the observation_data argument) and returning pyarrow.Table.

Other changes:

  • Moved online feature store support into public preview.

0.1.0b3 (2023.07.10)

  • Various bug fixes

0.1.0b2 (2023.06.13)

New Features:

  • [Private preview] Added online store support. Online store supports materialization and online feature values retrieval from Redis cache for batch scoring.

  • Various bug fixes

0.1.0b1 (2023.05.15)

New Features:

Initial release.

Metadata

Release files for azureml-featurestore 1.2.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for azureml-featurestore 1.2.2
File Interpreter ABI Platform
azureml_featurestore-1.2.2-py3-none-any.whl Python 3 none any Details

Release files / azureml_featurestore-1.2.2-py3-none-any.whl

Download URL azureml_featurestore-1.2.2-py3-none-any.whl
Size 166.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
fe3d6db4a100792cdce980248a2a97fb553a583a4162a60330420c844e51f527
BLAKE2b-256 checksum
How to use checksums
8f60cf2c37f832dfbfa84d34cba6ba5b04bf39b2e3310c070126dac83109997f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via RestSharp/106.13.0.0
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page