ML Feature Serving Platform

Summary

A low-latency feature serving platform for ML model inference, providing both batch (offline) and real-time (online) feature access with strict training-serving consistency. Redis serves the hot feature cache with p99 latency targets below 5ms for online inference requests; PostgreSQL provides point-in-time feature lookups for offline training jobs with temporal consistency guarantees; Cassandra stores high-cardinality feature entities at write scale beyond PostgreSQL's single-primary ceiling; Kafka streams feature computation events from online feature pipelines to update the cache; Qdrant stores vector features for embedding-based model inputs and similarity lookups; ClickHouse serves feature analytics and drift monitoring across training dataset populations. Feature versioning is first-class: every feature value is tagged with a pipeline_version and computed_at timestamp to support model reproducibility and training-serving skew diagnosis.

Problem Statement

ML feature serving sits at the intersection of two workloads with opposing operational constraints. The online serving path demands sub-5ms p99 latency for multi-feature lookups at high concurrency (model inference calls feature store 50–200 times per prediction request, and predictions are user-facing with strict latency budgets). The offline training path demands point-in-time correctness: the feature values retrieved for any training example must exactly match what the model would have seen at the time the label was generated, otherwise the model trains on features that did not exist at prediction time (training-serving skew). These two paths are often served from the same logical store but require completely different consistency and latency guarantees that cannot be simultaneously optimized on a single storage tier.