Best Practices for Feature Management in Palantir Foundry: Creating a Feature Store

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Hi all,

I’m curious about how others are managing features for their machine learning models within Foundry. We’re looking to create a feature store and would appreciate any best practices you can share.

I noticed that feature stores are mentioned in this PDF, but it seems to mainly reference the ontology as a feature store.

From my understanding, a dedicated feature store offers several capabilities that might not be fully covered by Foundry’s ontology and data pipelines, such as:

  1. Feature Versioning: Tracking changes to features over time for reproducibility.
  2. Feature Discovery and Reusability: A centralized repository for easy discovery and reuse of features.
  3. Online and Offline Feature Serving: Support for both real-time and batch feature serving.
  4. Feature Monitoring and Governance: Tools for monitoring feature quality, performance, and compliance.
  5. Integration with ML Pipelines: Seamless integration with machine learning workflows.
  6. Scalability and Performance: Optimized for handling large volumes of data and high-throughput serving.

Any insights or experiences with feature management in Foundry would be greatly appreciated!

Thanks!

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