Ask HN: Has anyone made the transition from app development to ML/AI work?

My approach has been to start at a high-level, with a specific goal in mind, and to progressively go deeper and deeper. The specific goal part has been really helpful IMO. It prevents sort of aimless shuffling about and provides a good metric to see if you're making progress. When I started I was basically just focusing on producing training data and treating the models, which were open-source on GitHub, as a black-box. At this point I've made a lot of modifications to the actual model code itself and I'm learning a ton. There's of course a bunch of adjacent skills that are similar to traditional backend skills, but slightly different. Like autoscaling for example, there aren't many autoscaling solutions for GPU VMs yet, there are some startups working on this space, but IMO it's good to have a rock-solid hosting solution that you don't have to worry about too much.

I opened up the beta of my product just last week at https://bondsynth.ai/signup

The goal is to either have my startup succeed or to move into an ML engineer position at a small-to-medium sized company.

Building the thing isn't supposed to be the challenge.

There are 2 levels to ML/AI, being a researcher and being an engineer. The researcher actually creates new models, architectures, etc. You're going to need to be talented at math, as well as pursue a PhD to have enough time to absorb some subset of the material to have a good understanding. (A masters was good but not enough time for me personally).

Then there is engineering which is leveraging the creations of the very smart PhDs. At least in my experience, the shallow level is basically fine-tuning models to your use case, which does require an understanding of some things like loss functions, train/validation/test sets, but it's not too complicated.

Everyone that asks me how to learn machine learning, I advise them to read Hands on Machine Learning by Aurélien Géron cover to cover. When I first started my masters I did this and it helped immensely because it was easy to understand, was broad, and was interested usually from an application perspective.

From there, I would suggest learning PyTorch (starting w/ Keras is ok too, but don't stay there too long, and avoid Tensorflow), as it's much easier to develop with. I always learn best with a personal project, so maybe see if there is a real life "problem" you'd be interested in solving, like classifying different pets from each other or something like that.

It'll take a while to build up your skills, so going to school is of course an option, but with dedication I think you can also accomplish this solely with side projects and learning on your own. Best of luck!