We are excited to announce the public preview of Azure ML managed feature store. Managed feature store empowers machine learning professionals to develop and productionize features independently. You simply provide a feature set specification and let the system handle serving, securing, and monitoring of your features, freeing you from the overhead of setting up and managing the underlying feature engineering pipelines.
By integrating with our feature store across the machine learning life cycle, you can accelerate model experimentation, increase the reliability of your models, and reduce your operational costs. This is achieved by redefining the machine learning DevOps experience.
This is a quote from our customer:
“Azure Machine Learning managed feature store allows us to build more accurate and robust AI models, achieving unprecedented performance for money laundering and fraud detection use cases. The capability allows us to easily create, store, and access features for machine learning. We are excited to be working with Microsoft to continue to push the boundaries of AI development and look forward to the possibilities from this new innovation.”
-Nicolas Goosse | Head of Artificial Intelligence | Belfius
How feature store optimizes your team's workflow
Discover and reuse features across your organization
You can search and reuse features across feature stores. You can also use the feature store from spark environments including Azure ML workspaces and Azure Databricks.
Create features with transformations
Materialize features
Materialization is the process of computing feature values for a given feature window and persisting in a materialization store. Now feature data can be retrieved faster and more reliably for training and inference purposes.
MLOps support
View lineage
For a given feature set, you can see the list of models that depend on it. For a given model, you can also see the list of feature sets it depends on.
Secure features
Summary
Managed feature store lets your machine learning team develop and productionize features independently while making your machine learning lifecycle more agile. Take it for a spin here
Learn more
To learn more, watch the Microsoft Build 2023 sessions to get familiar with other Azure Machine Learning announcements.
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