· Data Platforms · 1 min read
Feature Stores: The Bridge Between Data Engineering and ML
Why do your models work in the notebook but fail in production? Often, it's 'Training-Serving Skew'. A Feature Store is the fix.
Here is a common failure mode:
- Training: The Data Scientist writes a complex SQL query to calculate “Average User Spend (Last 30 Days)” using historical data. They train a model. It works great.
- Production: The engineer has to re-write that logic in Java/Python to calculate it in real-time for the app.
- Disaster: The Python logic is slightly different from the SQL logic. The model receives different inputs. It makes bad predictions. This is Training-Serving Skew.
The Solution: Define Once, Use Everywhere
A Feature Store (like Feast, Tecton, or Databricks Feature Store) is a central repository for these logic definitions.
- You define the feature
avg_spend_30donce. - Offline API: When training, the store provides a historical CSV of what that value was at that point in time.
- Online API: When the app runs, the store provides the current millisecond-fresh value from a fast cache (Redis).
It’s a Repository, Not Just a Cache
Crucially, a Feature Store allows teams to Share features. The Fraud team builds a “User Risk Score”. The Marketing team can now just use that score in their own models without having to rebuild the pipeline.
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