Full Stack Deep Learning, or FSDL, exists to solve a problem that anyone who has finished a theory heavy course runs straight into, which is that knowing how to train a model tells you almost nothing about how to run one in the real world. The course is built and taught by people with serious practitioner backgrounds, including Josh Tobin, Sergey Karayev and Pieter Abbeel, and it walks through the whole lifecycle that the academic courses tend to wave away. That means setting up projects and infrastructure, managing and versioning data, testing and troubleshooting models, deploying them, and then the unglamorous but critical work of monitoring them once real users and real data start hitting them. It has also kept pace with the field, folding in large language models and the tooling that has grown up around them, so it does not feel like a relic of an earlier phase of machine learning.
I think of it as the bridge between the notebook and the job, and there are not many resources that cover this ground as clearly. The honest caveats are about fit and shelf life. This is emphatically not a beginner course, and it assumes you already understand how models work and can build one, so someone at the very start should do a foundational course first and come back. Because MLOps tooling changes so quickly, some of the specific product recommendations will age faster than the principles behind them, though the principles are the point and they hold up well.
Working through the free recordings on your own is excellent value, but you do lose the hands on project feedback and cohort accountability that the paid live versions gave people, and a handful of topics are broad introductions rather than exhaustive treatments. None of that dents my overall view. For the specific and very common situation of being able to train a model but not knowing how to make it a dependable product, Full Stack Deep Learning is one of the most useful things you can watch, and the fact that the core material is free makes it an easy recommendation.