DeepLearning.AI · on Coursera
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.
Engineers and ML practitioners. This is an intermediate course in MLOps & AI Engineering, running about ~11 hours (3 modules).
Free to audit · certificate with Coursera subscription (from $49/mo). Included with Coursera Plus; financial aid available. Successor to the retired MLOps Specialization intro course. Seen 2026-11-01.
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.
An end-to-end LLMOps build pairing written lessons with code: data crawling and change-data-capture, a streaming feature pipeline with advanced RAG, instruction-dataset generation, fine-tuning and evaluation, and a deployed RAG inference service with prompt monitoring. It suits ML and backend engineers who want to see every production component of an LLM system, not just the prompt.
Google's official prep path for the Professional ML Engineer certification, covering Vertex AI, feature engineering, pipelines, and production ML systems with hands-on labs and skill badges. Best for engineers deploying ML on Google Cloud or targeting the certification.