Systems That Last.

Strength doesn't need to be loud. It needs to be steady.

Powering Models: Feature Engineering, Part 2

Getting features to the model at inference time: passing them in the request or fetching them from a store. Both choices have consequences.

Part 2 of 2. How production services make features available to models at inference time: the tradeoffs between request-time feature passing and online feature store retrieval, with patterns from real systems.

Posted by Varchaleswari Ganugapati on Wednesday, May 13, 2026

Powering Models: Feature Engineering, Part 1

From raw events to training-ready features: the offline pipeline that makes production models possible.

Part 1 of 2. A deep dive into the offline half of the ML lifecycle: EDA, the DS to engineering handoff, feature pipelines on Kafka and GCS, and automating it all with orchestration tools.

Posted by Varchaleswari Ganugapati on Wednesday, May 13, 2026