[ativzzz]

As a web developer, you are primed to create the interfaces to these ML tools (or any tool really). The browser is the most widespread UI application that nearly every single consumer computer has access to. You can work on literally anything that someone accesses via the internet, and the more AI/ML is made useful, the more people will need access to these products, the more web devs will be needed to build apps to use these products

[imiric]

Not only for building user-facing tools; the AI/ML space needs a lot of good engineering practices to function. In many cases, traditional software, infra and DevOps, QA and UI engineers, etc., are as crucial to ML projects as data scientists are.

So I don't think you need to worry about being left behind or that your skills are stagnating if you're not directly developing ML models. There's no existential reason to jump in, unless you're particularly interested in ML.

[alexose]

> So I don't think you need to worry about being left behind or that your skills are stagnating if you're not directly developing ML models.

Aren't ML models kind of the front lines these days? If I'm understanding correctly, it's the models, the training techniques, the curation of data sets-- These are the things that will inform the next generation of products and services.

I agree that there's more to it that just letting ML models loose, but it certainly seems like the core of it.

[imiric]

Depends on what you want to focus on. My point is that there are plenty of roles adjacent to the core of ML that are still needed to make ML function. Think about data storage for models, maintaining CI pipelines for training, UIs for curation and labeling, packaging and deploying models, data version control systems, etc. None of these are tasks data scientists should be concerned about, and viceversa, data science is not something engineers should necessarily be concerned about either. It doesn't make either role superior; they just complement each other well.

[yurishimo]

This makes me hopeful for the future of my career, but as a n00b in regards to CS principles, it still kinda sucks that the minutia of this technology is so foreign to me. I think that is what the OP was getting at.

Using ML/AI tools is not terribly difficult, and the principle of how they work is simple enough (feed the model examples of what you want, eventually it can reproduce similar ideas when prompted or recognize new examples as something that it's seen before). Maybe it's ignorance on my part in regards to what it takes to learn these technologies deeply, but right now, I wouldn't even know where to begin to start studying to really learn how this technology works/operates on a fundamental level.

Despite my ignorance of the subject, I could probably work out how to step into an ancient archaic COBOL system or whatever, but ML/AI _feels_ so far out of my reach as a webdev.

This is one reason I think ML as a topic is cool, even if I think the practical uses for the every day person is still really far away from being a realistic.

[andai]

As a wise dog once said, "sucking at something is the first step towards being kinda good at something."

I avoided many areas of computer science and programming for far too long because I thought they were "hard". Many of them turned out to be way easier than I expected (I often found myself wondering, "why didn't someone tell me how easy this is! I could have done this years ago!!"), or so much fun that working on them felt effortless, despite the increased effort in objective terms.

I guess what I'm saying is, start learning about what interests you today! Make small and consistent progress.

I've found that setting aside time every day (rather than just when I feel like it) to study areas of interest has been extremely helpful in this regard.

[heavyset_go]

It's not something that's out of reach for a web dev. It might seem like an insurmountable task, but if you break it down, you can digest it easier.

ML is just math. We're at a point where you don't even have to completely grok the math to apply ML techniques productively.

If you want somewhere to start and need a project idea, read up on how to build a simple binary classifier. The "hard" work is building good training and validation sets if you use a ML framework.

[escape_goat]

The practical deep learning course from fast.ai is what I've been working my way through, at a much slower pace, I am sure, than someone would if they were writing code for a living. The difficult technical hurdles are getting used to (i) Python if you've never used it and (ii) Jupyter notebooks if you've never used them. I'm using Paperspace.com for my GPU instances.

https://course.fast.ai/

[yurishimo]

Thanks for the link! I'll put it on the list of other stuff to check out. I don't write Python at work, but it's probably something I should familiarize myself with a little more since it's pretty popular in the Home Automation communities.