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Machine Learning in Production (CMU Course)

17-445/17-645/17-745) / AI Engineering (11-695) Spring 2025 CMU course that covers how to build, deploy, assure, and maintain software products with machine-learned models. Includes the entire lifecycle from a prototype ML model to an entire system deployed in production.

For researchers, educators, or others interested in this topic, we share all course material, including slides and assignments, under a creative commons license on GitHub ( https://github.com/mlip-cmu) and have also published a textbook with chapters corresponding to almost every lecture. How can we identify and measure important quality requirements, including learning and inference latency, operating cost, scalability, explainablity, fairness, privacy, robustness, and safety? An extended group project focuses on building, deploying, evaluating, and maintaining a robust and scalable movie recommendation service under somewhat realistic “production” conditions with 1 million users.

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