Full Stack Deep Learning

Full Stack Deep Learning (FSDL) is a free, project-based MLOps course that teaches how to ship deep-learning-powered products end to end, not how to train models from scratch. The latest edition, FSDL 2022, is taught by Sergey Karayev and Josh Tobin (with Charles Frye) and consists of nine lectures plus eight hands-on labs and a capstone project covering development tooling, testing, data management, deployment, continual learning, foundation models, ML team management, and ethics. It is genuinely advanced: the official prerequisites are at least one year of Python, prior completion of a deep-learning course, and comfort with Unix and version control, and the lectures explicitly skip the fundamentals. The content is strong but frozen at 2022, so newer LLM/agent tooling is only partially covered (the separate 2023 LLM Bootcamp fills some of that gap). It is best for working engineers and data scientists who already know the math and want the production layer, and a poor fit for beginners looking for an intro to neural networks.

FSDL is an excellent, free, practitioner-grade MLOps course, but only if you already meet the steep prerequisites (a year of Python plus a prior deep-learning course) and accept that the most recent materials are from 2022. Beginners or those wanting up-to-the-minute LLM tooling should look elsewhere or pair it with newer resources.

Best for: Software engineers moving into ML, ML/data-science practitioners who want to strengthen the production and engineering side (deployment, testing, data pipelines, monitoring), MS students, and PMs on ML teams who already understand deep-learning fundamentals and can program in Python.

Skip if: Complete beginners or anyone who has not yet taken a deep-learning course. The lectures deliberately do not teach gradient descent, backprop, CNNs, or transformers, and assume you already know PyTorch. People wanting a current, end-to-end LLM/agent course as their primary goal will find the 2022 material partly dated.