Google Cloud · on Google
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.
Engineers and ML practitioners. This is an intermediate course in MLOps & AI Engineering, running about 17 activities, self-paced.
Free to browse courses · hands-on labs require credits or subscription. cloudskillsboost.google now redirects to skills.google; 17 activities. Exam fee for the certification is separate.
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.
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.