{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Provides Python SDK access to Azure ML Feature Store for developing feature sets, managing feature specifications, and running offline feature retrieval with point-in-time joins.","skillfed_tags":["azure-ml","feature-engineering","ml-infrastructure"],"use_cases":["Define and manage feature sets in Spark with custom transformations for ML model training pipelines.","Retrieve historical feature values at specific points in time for training dataset generation.","Materialize computed features from offline storage into online Redis cache for batch scoring.","List and inspect feature specifications already defined in your Azure ML Feature Store.","Build feature engineering workflows using DSL syntax without writing custom transformation code."],"what_it_does":"The azureml-featurestore package is the Python SDK for Azure ML's managed feature store, designed to work alongside azure-ai-ml. It lets you define feature sets with Spark-based transformations, list and retrieve feature specifications, and run offline feature retrieval using point-in-time joins\u2014a key pattern in ML pipelines where you need historical feature values aligned to specific timestamps.\n\nThe package supports multiple feature definition approaches: a Domain Specific Language (DSL) for declarative transformations, user-defined functions (UDF), or no transformation. It can load from materialized stores, handle temporal joins with lookback windows, and materialize data between offline and online stores. Runtime dependencies include azure-ai-ml (the parent SDK), mltable (for table abstractions), jinja2 (for templating), marshmallow (for serialization), and pandas (for data handling).","worth_installing":"Yes, if you are already invested in Azure ML and need a managed feature store with offline retrieval and point-in-time join capabilities. The package is production-stable, has low install friction, and carries permissive licensing. However, the 189-day gap since last release and aging maintenance status suggest slower iteration; verify that the current feature set (including online store maturity) meets your timeline and requirements before committing to it for new projects."},"id":"azureml-featurestore","links":{"html":"https://skillfed.io/packages/azureml-featurestore","md":"https://skillfed.io/packages/azureml-featurestore.md","pypi":"https://pypi.org/project/azureml-featurestore/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2026-02-06","license_spdx":null,"license_treatment":"permissive","name":"azureml-featurestore","python_support":"supports_current","summary":"Azure Machine Learning Feature Store SDK"},"popularity":{"monthly_downloads":1128892,"position":4322,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.2.2"}