This site explores the engineering of production machine learning systems — architecture, scaling constraints, reliability, and failure modes. The focus is on practical trade-offs and real-world system behavior beyond notebooks and demos.
It also covers broader machine learning engineering practices and serves as a companion resource for an MLOps course on designing, deploying, and operating ML systems in production.