Covers the full lifecycle of a deep learning product: infrastructure and tooling, troubleshooting, data management, deployment, monitoring, and ethics, with labs building a text recognizer app. Ideal for ML engineers who can train a model but need to ship one.
Who it's for
Engineers and ML practitioners. This is an intermediate course in MLOps & AI Engineering, running about 9 lectures plus labs (~12 weeks).
Cost at a glance
Free. All lecture and lab material free forever per the site.
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DeepLearning.AI·Coursera
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Andrew Ng's concise course on the ML project lifecycle: scoping, data-centric iteration, error analysis, deployment patterns, and monitoring. A fast, high-signal introduction to production thinking for anyone who has only trained models in notebooks.
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Certificate
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Goku Mohandas's lesson series walks through designing, developing, deploying, and iterating on a production ML application with experiment tracking, testing, versioning, serving, CI/CD, and monitoring. One of the clearest end-to-end MLOps walkthroughs for engineers moving from notebooks to production.
intermediate
Self-paced (foundations + MLOps lessons)