Feast feature store setup for consistent ML features

I build and operate ML feature stores on Feast so your models see exactly the same features in training and in production. Training/serving skew usually creeps in quietly: an offline pipeline and an online API compute the same feature slightly differently, and by the time it shows up in your metrics it is painful and slow to trace. I remove that whole class of bugs by defining every feature once and serving it from a single source of truth.

My work covers the full setup: entities and feature definitions, an offline store for historical training data, an online store for low-latency serving, and point-in-time correct joins so your labels never leak information from the future. I wire the ingestion pipelines, schedule materialization, and add monitoring so you know the moment a feature goes stale or starts to drift. Everything lives under version control and is fully reproducible.

You end up with a clean feature registry your data scientists can reuse across projects, a faster path from experiment to production, and numbers that match on both sides. I can start from an empty repository or untangle an existing half-built store, and I document every step so your team can own and extend it after I hand it over.