Databricks · on Databricks Academy
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.
Engineers and ML practitioners. This is a beginner-level course in MLOps & AI Engineering, running about ~12 hours.
Free (self-paced path, ~12 hours). Labs-included subscription and instructor-led classes sold separately; free path includes Get Started with ML, Data Prep, Model Development, Model Deployment, and ML Operations courses.
Chip Huyen's course notes and slides on designing reliable, scalable ML systems: data engineering, feature stores, training infrastructure, deployment, monitoring, and continual learning, later expanded into her O'Reilly book. Read it to think like an ML systems designer rather than a model trainer.
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.