#Feature Stores

Articles about Feature Stores — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with feature stores. ← Blog home

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Pratik Dhanave · ·4 min read

Features and Feature Stores — Where Fraud ML Is Actually Won

Ask practitioners where fraud detection quality comes from and the answer is uniform: not the model — the features. Velocities, histories, graph signals, and their freshness carry the signal; the model just reads it. And ask where production fraud ML fails and the answer is equally uniform: the gap between how features were computed in training and how they're computed at serving time. This post covers both — the feature families that catch fraud, and the feature-store discipline that keeps them honest.

Signal lives in velocities (second-fresh streaming aggregates), histories, precomputed graph neighborhoods, and composed scores. The characteristic failure is training/serving skew — feature time-travel leaking the future into training. The feature store fixes it by construction: one event-time definition, two materializations.

#Fraud Detection#Feature Stores#ML Infrastructure#Machine Learning#Risk#Streaming

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse the blog or explore related topics using the tag cloud above.