Everything I know about ML is self taught. My local Uni has a ML Engineering degree that's relatively new. Every once in a while I look at the curriculum, outside of some higher lvl math, 90% of that four year degree consists of topics I "mastered" in the first 6-12 months of self study trying to build prediction models for various sports.
If I could go back I would start by reading Josh Starmer's Statquest Guide to Machine Learning, and then his guide to AI/Nueral Networks[0]. Starmer does the best job at explaining advanced ML topics in a very beginner friendly way, the books are literally written in the format of a children's book.
Then just start tinkering. I got interested in ML because of sport's analytics and betting markets so I read a lot of papers on that topic and books similar to Bayesian Sports Models in R by Andrew Mack[1].Also, Jake VanderPlas's Python Data Science Handbook is good[2].
Ideally, find a vertical you're interested in where experts have applied ML and read their papers/books and work backwards from there.
Do you know how to optimize the MTU of an Infiniband interconnect? Do you understand how to schedule multiple models being trained concurrently? Do you know why you cannot directly leverage bare metal compute within a Docker image?
This is important Infra knowledge you need to take full advantage of any model you are training on your own hardware. And this is why Deepseek was successful - they understood the ins-and-outs of systems programming and the H800 architecture to maximize the compute performance they needed to train their model.
What about Linux namespaces makes you think it's not running on bare metal? Maybe you are thinking of versions of docker that introduce a Linux hyper visor like macos?
