Many machine learning projects succeed in notebooks but fail in production. This talk explores the end-to-end journey of turning an ML prototype into a reliable, scalable, and maintainable production system.

We’ll walk through the full ML system lifecycle—from problem formulation and data pipelines to model training, deployment, monitoring, and iteration—highlighting the engineering and organizational decisions that matter most. The session emphasizes real-world constraints such as latency, cost, data drift, observability, and failure handling, rather than purely model-centric optimization.

Attendees will gain a systems-level perspective on ML, learning how infrastructure, data, models, and humans interact in production environments. The goal is to demystify “production ML” and provide a practical mental framework for designing ML systems that actually survive contact with reality.