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Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

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Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#21

Post by minimaxir »

Speaking from experience, almost all FAANG positions I've seen require a degree for ML/AI, and even require a degree for less research-oriented positions like a Data Analyst/Scientist.

Non-FAANGs may be less picky but the competition in the field is too great at the moment (due to MOOCs/Bootcamps increasing supply), and even with an excellent portfolio it may be impossible to stand out. (in my case, despite my data science "fame" most recruiters tossed my resume out immediately during my job hunt a year ago; the only interviews I got were by going above the recruiters. And that was for data science, not even ML/AI)

Even after working as a Data Scientist for over a year, I've received practically no recruiter spam.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#22

Post by ultrasounder »

tmsh wrote: post #18

https://aws.amazon.com/training/learning-paths/machine-learn... Patience. Spending that extra time (it helps if you really enjoy it or can program yourself to really enjoy it). https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700... for inspiration. To answer the question though: I'm not sure what you produce other than maybe blog or publish some analysis using your data skills. And maybe: https://github.com/…

Thanks for the links to AWS videos. These were posted here on HN sometime back. Will definitely bookmark them and come back to them. At the moment(literally)i am still finishing up Udacity PyTorch and hope to continue wit the venerable DL4Coders part1 and part2. As someone else had posted, coming up with useful implementations of popular ArXIV papers seems to be one sure shot way of building up a personal brand on Github.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#23

Post by tmsh »

ultrasounder wrote: post #22
tmsh wrote: post #18

https://aws.amazon.com/training/learning-paths/machine-learn... Patience. Spending that extra time (it helps if you really enjoy it or can program yourself to really enjoy it). https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700... for inspiration. To answer the question though: I'm not sure what you produce other than maybe blog or publish some analysis using your data skills. And maybe: https://github.com/…

Thanks for the links to AWS videos. These were posted here on HN sometime back. Will definitely bookmark them and come back to them. At the moment(literally)i am still finishing up Udacity PyTorch and hope to continue wit the venerable DL4Coders part1 and part2. As someone else had posted, coming up with useful implementations of popular ArXIV papers seems to be one sure shot way of building up a personal brand on Gi…

Cool yeah! I listened to the first three courses (Math for ML / Linear and Logistic Regression / Elements of Data Science) on my commute, and even though I thought I knew things, just persisting in "re-learning" the material helped a lot to fill in the gaps for me. I was just browsing the links again too, there's a whole lot of other courses/specialities after that too (if not posted there, maybe elsewhere).

I am not an expert, but from what I've heard/seen, being really solid on the fundamentals of regression and feature modeling (and not being afraid to read and apply ArXiv papers) are all key. And eagerness and statistics go a long way and are valuable to companies.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#24

Post by kk58 »

I built ML application in my domain which helped demonstrate my capabilities.Networked a fair bit within my company and then got the chance to lead a ML team. Whole process took about 2 years.meanwhile self educated myself continuously over last 3 years. Spent min 20 hours a week coding, reading, learning and discussing ML. Joined ML learning groups helped other folks and learned through their journeys as well.

ML is unfortunately better done in a big company due to data but also a b*Ch due to tremendous friction within org to get things done.

Another key strategy is to commit yourself to build an end 2end ML application, structure your learning around it. I found this a tremendous technique to turbo charge my learning.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#25

Post by teacpde »

xenihn wrote: post #12

Maybe someone who actually works at FAANG can weigh in, but I would think that one of your best bets would be getting into one as a general SWE and then transitioning to AI/ML internally after a year. I recall Google even having some sort of internal program that encouraged this. Getting into Google is a moonshot, but it's possible to do so with no prior professional programming experience if you put in a ton of effo…

Internal transition is always easier, I have several colleagues went from SWE to ML related roles this year. One thing worth noting is that most of them work on building ML infra/platform rather than direct user facing ML features, so it is not drastically different from what they did previously.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#27

Post by souljaboytylem »

After reading most of the comments I can try to provide a different perspective.

I am a Director of Data Science and Software Engineering for a mid sized firm (~1000 employees and $150-200MM revenue). I started with a Finance degree then shifted into an analysis position at a FAANG (lots of excel, SQL, learning how to query big data). This eventually led to learning more about tech (python, AWS cloud stack, messaging queues) and after 8 years in the industry giving me enough experience to manage teams of data scientists, software engineers and data analysts.

Although it is so important to know all the software engineering stack, many companies will benefit from simple business intelligence and data analyst roles. I guess my recommendation is to also keep an open mind in looking for these types of roles in the market (data analyst, business intelligence engineer), because given your desire to learn and existing background, its clear you can make a big impact in those companies as well. And it will be much less competitive than traditional CS crowd.

Some food for thought

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#29

Post by ultrasounder »

pps43 wrote: post #28

EE -> embedded programming -> programming -> financial software -> predictive modeling -> ML.

Wow!. That's got to be the most scenic route to ML, though one might actually get exposed to a lot of Core technologies on the way.

Re: Ask HN: Engineers from non-CS background, how did you pivot into ML/AI?

#30

Post by ryanjodonnell »

xenihn wrote: post #12

Maybe someone who actually works at FAANG can weigh in, but I would think that one of your best bets would be getting into one as a general SWE and then transitioning to AI/ML internally after a year. I recall Google even having some sort of internal program that encouraged this. Getting into Google is a moonshot, but it's possible to do so with no prior professional programming experience if you put in a ton of effo…

Random side note, but when is the 'FAANG' acronym going to die? MSFT is killing it, prob the top tech company around these days. Needs to be included in that list.
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