feature store

Online models need features that are fresh and computed the same way training computed them. The classic failure is training-serving skew that quietly degrades the model. The signal is the online/offline split built on one shared definition. Here is the design.

Classic distributed-systems tradeoffs appear in ML infra with an ML twist: stale features and eventually-consistent reads carry model-accuracy consequences, not just correctness ones. Here is how to reason about it.

Personalizing an LLM is a retrieval and memory problem, not a per-user fine-tune. Learn how to assemble user context at request time, keep long-term memory from bloating the prompt, and respect privacy and the right to be forgotten.

Raw clickstream lands in object storage and somehow turns into clean tables, dashboards, and ML features. The design questions are table format, the bronze-silver-gold layering, batch versus streaming, and how you handle schema drift and late data. Here is the lakehouse blueprint.

A feature store is simple to define and simple to botch. The signal is naming the precise bug it prevents (training-serving skew) and the point-in-time correctness that blocks label leakage. Here is the full picture.

Same model, two answers: clean offline, ugly online. The cause is almost always a feature computed differently across the two paths. Here is the diff-based hunt that localizes it to a single column.

A fraud model fed a feature an hour behind is half-blind, but recomputing everything in real time wastes money you don't need to spend. Freshness is a per-feature decision on a real cost curve. Here is how to manage it.

A data engineer renames a column and three production models quietly begin scoring on garbage. Features form a shared contract, and altering them demands the same care as a breaking API change. Here is the discipline it takes.