Duke University · on Coursera
Noah Gift and Alfredo Deza teach Python for DevOps, cloud ML platforms, MLOps tooling, and LLMOps across four courses. Suited to engineers with some coding background who want a university-branded MLOps credential.
Engineers and ML practitioners. This is an advanced course in MLOps & AI Engineering, running about 6 months at 5 hrs/week (4 courses).
Free to audit · $49/mo Coursera subscription for certificate. Included with Coursera Plus; financial aid available. Seen 2026-11-01.
Databricks' free role-based path takes you from getting started with ML on the platform through data preparation, model development, deployment, and MLOps with MLflow. Useful for engineers whose employers run on Databricks or who want badge-backed lakehouse MLOps skills.
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.