feature store

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

Classic distributed-systems tradeoffs show up in ML infra with an ML twist: stale features and eventually-consistent reads have 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, manage long-term memory without bloating the prompt, and respect privacy and the right to be forgotten.

Raw clickstream lands in object storage and somehow becomes 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 easy to define and easy to get wrong. The signal is explaining the exact bug it prevents (training-serving skew) and the point-in-time correctness that stops label leakage. Here is the full picture.

Same model, two answers: clean offline, ugly online. The culprit is almost always a feature computed differently in 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 burns 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 silently start scoring on garbage. Features are a shared contract, and changing them needs the same care as a breaking API change. Here is the discipline.