ML Feature Engineering Pipeline

The Challenge

Data scientists were spending most of their time hand-rolling one-off feature extraction scripts per model, with no shared definitions. The same "customer lifetime value" feature was computed three different ways across three different models, and nothing was reproducible outside the original author's notebook.

The Approach

The fix was to treat features as a shared, versioned product rather than a per-model scratch file.

• Centralized feature logic into versioned PySpark jobs running on Databricks, reading from and writing back to Delta Lake.
• Defined a shared feature store layer so multiple models could reuse the same validated features instead of recomputing them.
• Wrapped every pipeline run in MLflow experiment tracking, automatically logging parameters, metrics, and artifacts.
• Registered production-ready models in the MLflow Model Registry with a clear staging-to-production promotion process.

The Results

Feature work stopped living in scattered notebooks and became something the whole data science team could build on together.

Key Takeaways

A shared feature store pays for itself the moment a second model needs the same signal. Pairing it with MLflow tracking meant every result could be traced back to the exact code, data, and parameters that produced it — no more "it worked on my laptop."