How do you break into a career in machine learning? (2020) • Hacker News Board

Re: How do you break into a career in machine learning? (2020)

#61

Post by jeffwask » Fri, Aug 05, 2022, 4:09 PM UTC

Test0129 wrote: post #29

jeffwask wrote: post #8

MLOps is a good way for someone with a Cloud Engineering or DevOps skills set to join a team and start learning from within

In my experience no matter the field breaking into it via ops is almost a certain way to never get to where you want to be. Cloud Engineering/SRE/MLOps are so in demand a company would be foolish to let you move up unless you really, really made a stink about it. Better to just get your MSc in statistics/CS. It's possible to break into the field with less but of the (very talented) ML engineers/scientists I know the…

>In my experience no matter the field breaking into it via ops is almost a certain way to never get to where you want to be. Cloud Engineering/SRE/MLOps are so in demand a company would be foolish to let you move up unless you really, really made a stink about it.

You need to pick better companies. If the place you work handles employee growth and development by saying this employee is to valuable to support their career then gtfo.

Re: How do you break into a career in machine learning? (2020)

#62

Post by victor106 » Mon, Aug 08, 2022, 2:52 AM UTC

angarg12 wrote: post #36

victor106 wrote: post #19

Earlier quoted context omitted.

As a Machine Learning Engineer what tools do you use on a regular basis?

Spark in Scala, Pytorch, Docker, bunch of infra... We run in AWS and have a ton of proprietary tools.

thank you.

Re: How do you break into a career in machine learning? (2020)

#63

Post by CactusOnFire » Tue, Aug 09, 2022, 4:20 AM UTC

sateesh wrote: post #31

CactusOnFire wrote: post #5

Earlier quoted context omitted.

I'm a Data Scientist who builds ML models. My bachelor's is in psychology, I just studied and learned how these algorithms work.

It will be interesting if you can share how you landed in your first ML job, once you learnt the algorithms. I think getting the first job in ML role, if you don't have formal qualification in the related field is the hard part.

Got my foot in the door doing an after-school program teaching kids to code.

I leveraged that to get a teaching assistant job at a bootcamp for adult professionals.

I networked my arse off at the teaching assistant job until experienced programmers (such as instructors) realized I knew my stuff but was underemployed. I got a couple of side gigs doing BI Analytics that way.

After doing this, I had a tough set of interviews for my first full-time role. Every failed interview taught me about my weaknesses and blindspots, and I learned from them. I opted to get stronger at system design, stats & ML algorithms, though I feel like grinding leetcode could have been another approach at this point.

Because I had a wide set of marketable skills within data-oriented work, an analytics consulting firm took a liking to me. I had versatility for billable projects, and I got a bunch of tech certifications in AWS/etc. This role would be describable as 'Analytics Engineering'.

They overworked me for a little while, then my next role was a Data Scientist role that was on my own terms.

I don't want to make it sound like I could just jump in no problemo. I had to think strategically about how to climb each rung of the ladder. But I am now at a point where I have the experience needed to be a senior. While some companies might turn me down for not having a piece of paper, there are enough who actively want me that I am sitting pretty with my career.

Re: How do you break into a career in machine learning? (2020)

#64

Post by jxm262 » Thu, Aug 11, 2022, 1:23 PM UTC

drowning_sushi wrote: post #35

jxm262 wrote: post #27

Earlier quoted context omitted.

This is literally opposite of me. Would you mind elaborating a bit on the negatives of working on the ML side of things? Definitely not saying your wrong or that it's better than backend dev (it's probably just personal preference). But as someone considering it, I'd like to hear the good and bad of each type of role.

I too moved away from ML after actively pursuing it for many years. YMMV but here are my reasons - Scientists dont always make the best 'clients'. The requirements you spend months implementing may be completely obsolete by the time you are done and then completely unused. - You often dont understand or are made aware of the impact of your work. - Its challenging to compete with Masters/Phd graduates who have spent y…

Thanks for sharing your input here.