There is a lot here: 3 roadmaps, guides for 89 tools, free live sessions and open courses. Pick whichever of these sounds most like you and we will tell you exactly where to begin.
At university, or self-taught, and you have trained a model in a notebook but never shipped one.
Start from the foundations and work towards being job-ready. Expect 6–9 months at 10–15 hours a week.
No session is scheduled right now — the community channels announce the next one first.
You already work in DevOps, SRE, platform, infrastructure or backend, and you want to move into ML.
You have more of this than you think. Fill the ML gap, then learn the tools that are specific to it.
No session is scheduled right now — the community channels announce the next one first.
You are a junior or mid-level ML or MLOps engineer, and you want depth rather than another tutorial.
Go deep on production concerns — serving, observability, scale and cost — and on LLMOps.
No session is scheduled right now — the community channels announce the next one first.
You are at a company that needs ML people, ML training for the team you have, or a system actually delivered.
We train, staff and build. The paid work for companies is what keeps everything else free.
No session is scheduled right now — the community channels announce the next one first.
Nothing here is a commitment, and nothing is behind a paywall. Open the roadmap that sounds closest, read the first guide, and come and ask in the community if it turns out to be the wrong one.