Ask HN: Is it realistic to want to get a job in AI/ML/DL without any degree? | Hacker News Reader

Ask HN: Is it realistic to want to get a job in AI/ML/DL without any degree?

5 pointsthrowawaynay4 comments

Hi, I've had a few jobs as a web developer, mostly in startups. I spent ~2 years in college doing software engineering(mostly C, algorithms/DS, web dev, but I started coding a long time before college) I got offered a job before graduating and needed the money so I couldn't stay in school.

Do you think if I spent 6 months or +1 year on MOOCs/Books/personal projects, I could land a decent job as a junior ML engineer or something similar?Not at a FAANG or anything like that. Just a decent job.

Or do you think it's absolutely necessary to go through a Master/PhD to get a decent job in that field? I honestly don't think there is a (great)future in web dev plus it's starting to bore me, so I want to switch.

Btw I'm based in western Europe.

Thanks

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I think that is very doable, companies used to need a team of masters / PhDs to implement the ML stuff more or less from scratch, but libraries are nowadays very advanced and easier to use. Just start learning stuff that interests you, maybe keep them in a private git repo or even write a blog about them. It is realistic to aim for a junior position, and in the first few years you'll be learning a lot!

Many companies utilize AWS / Azure / Google Cloud, and they offer free tiers so that you can start using them basically for free. And with the help of virtualization you can even run a local minikube "cluster" and try running Airflow and whatnot there.

Although ML engineering has a bit different focus than say data scientist. But even in those roles it is useful to know how to program, do custom web UIs etc.

Definitely possible. Masters/PhD not necessary. We already have incredibly accessible tooling and with a little bit of reading, you can get a lot done.

I'm recently finished an ML project which had many people who had never done ML before this. By the end of the project the team was competent enough to ingest new data, perform EDA, annotate, fix annotations, train, generate and interpret metrics, experiment with different models, re-train and so on.

The tricky part is finding the org or the opportunity that has a need great enough that they will take you on.

Very realistic. Get involved in setting up data engineering pipelines. Apache AirFlow (Composer), BigQuery, Vertex AI, PySpark, Pandas, etc. Huge demand for these engineers now, most of whom have come out of DevOps.

Those jobs are very different from what OP is asking about.

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