Do you really want to, or do you just want lots of money? ML jobs are split into theoretical and practical. Theoretical involves building proprietary models b

Do you really want to, or do you just want lots of money? ML jobs are split into theoretical and practical. Theoretical involves building proprietary models based on academic papers, and training them. This is where the PhDs are going. Practical involves deploying ML models, either in the cloud or on devices. This doesn't require the heavy theory that is still rather new in university, it is more about application programming. The theoretical jobs pay a lot more, the practical jobs just require a cursory knowledge of ML, and not the nuts and bolts. The latter requires a lot more patience to understand the explosion of inference hardware (esp. Nvidia's convoluted tooling). Theory: can't unless you have a phd. Practical: learn python and tensorflow, C++, and devops.