[MWil]

Someone wants to get started in AI/ML today and they have beginner-level understanding of Python/Javascript. Without any further context, but a desire to learn AI/ML and building on what they know should that person next look to: 1) learn PostgreSQL, pgvector, and whenever the "new" comes 2) learn PyTorch, TensorFlow in Python 3) learn TensorFlow.js Presume hobby-level interest, not production-safe best practices - so I guess there is that additional context

[esafak]

Read papers about the problem you wish you to apply ML to, and implement them in pyTorch. I would get a book too. You could read the first three sections of this, for starters: https://probml.github.io/pml-book/book1.html

[hot_gril]

#2. Databases like Postgres are central to backend applications, but for AI the real action is happening on those tensor frameworks.

Honestly I can't see pgvector becoming a mainstream way of running inference. I used pg cube for that on one project a while ago. Yeah it worked, but even ignoring the performance issues, the only reason we considered it was because of our weird use case. We were also doing other funky stuff like large sparse matrix math using just float8 cols, with parallelism (by splitting one query into ~32).

[gorbypark]

Most likely #2 would be the best bet for both hobby level and above. You could do some fun stuff with Postgres and pgvector, but you'll be "limited" to creating some embeddings with an existing model, storing them in pgvector and again using that to add context to an existing model (more or less).

#3 would limit you to running existing models in a browser.

Only #2 would allow you to actually understand and create new meaningful models.