How to 🔥Boost🔥 Your MLOps with Feature Stores❓

We talked about data for ML: how it is different from traditional data-driven systems; and how the dual view of data at the training and servicing times can be a challenge for deploying your ML models. So now,

How to build a machine learning specific data system that can solve these problems?

✎ Solution #1. Easy pipeline creation with feature stores

Feature stores can make it easy to productionize data pipelines by authoring or publishing a locally developed feature transformation pipeline to the feature store, which can then be served both online and offline. The computation is registered in the feature store. The features will continuously or on some schedule compute and then persist copies of the data in order for training datasets to be built during consumption or for online values to be read out in production.

The key idea here is to let model developers focus on the business logic without having to think about pipeline scheduling or backfilling the data, or worrying about streamlining feature pipelines across different environments. A large part of the inconsistency challenge is addressed by applying the same logic to data run in streams, in batch on historical data or in real-time systems.


✎ Solution #2. Consistent data access

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