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Ask HN: How to pivot to a Machine Learning engineer?

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Re: Ask HN: How to pivot to a Machine Learning engineer?

#21

Post by breckenedge »

I dipped my toes in about 7 months ago with a 3-month-long project to make content recommendations using ML. I started with off-the-shelf collaborative filtering libraries and ended with PyTorch. ChatGPT was a huge help. I would have been OK continuing down that route, but execs wanted faster and better results, and 3 months is just enough time to really get a flow going when starting from no experience.

A lot of ML was cleaning up and preparing datasets, which wasn’t that much fun. I had an exec pushing for using Amazon Personalize, which I gave a good try but ultimately it didn’t deliver. Was that because of data problems or underlying models? That’s the crux of the problem when using black-box ML services: you can’t analyze what’s going on. And Amazon Personalize makes changing the data layer a pain, so you never know if you’re getting closer to a better solution. Who knows, Personalize is probably great in the hands of experienced ML folks.

So, if you can swing it, definitely recommend doing an ML trial project to see if you like it before committing your career to it.

Re: Ask HN: How to pivot to a Machine Learning engineer?

#22

Post by vasili111 »

jampekka wrote: post #11
harimau777 wrote: post #5

Earlier quoted context omitted.

When people talk about working with ML requiring a lot of math, what do they mean? That is to say, I have a degree in electrical engineering so I learned a lot of math. However, my programming career hasn't required me to actually use it since I graduated. So I understand/"know" a lot of math. However, it would be tough for me to build back up to the point where I can, for example, solve differential equations again.…

> Does a career in ML require a strong understanding/knowledge of math or does it require you to be able to solve a lot of math? No. With ANNs, unless you're doing research into totally new ML architectures, you're not gonna do any maths apart from some arithmetic. And even if you do, it's usually quite simple maths, mostly matrix multiplications and simple non-linear transformations of scalars. Nothing even close to…

Just to he sure that I understand the context, are you working as MLE or where from you had that knowledge?

Re: Ask HN: How to pivot to a Machine Learning engineer?

#23

Post by nsypteras »

> Is there an ML engineering practice that isn't focused on building models but more on managing/deploying/scaling models? i.e. can I avoid learning all the maths underneath?

I transitioned to this in 2018. It was called MLOps. I was a mobile developer before that. Transitioning was pretty easy at that time (it might be more competitive now). What I did: worked on an intensive ML project for myself and realized I enjoyed working across the ML stack. I wrote a blog post on the project here: https://www.nicksypteras.com/blog/aisu.html. Then I applied to an MLOps team and leveraged the project to demonstrate skills/experience. As for avoiding the math, you could probably get away with it but learning the basics will make everything way easier. I feel like without some basic ML math under my belt I would have been flying very blindly.

Re: Ask HN: How to pivot to a Machine Learning engineer?

#24

Post by vasili111 »

ngc248 wrote: post #4

I transitioned into an ML Engineer. There are different types of roles in an ML project. 1. Data scientists - These are the people who analyze the data and prepare the models. They basically deliver a jupyter notebook to us 2. ML Engineer - We take the notebooks from the Data scientists and productionize it. 3. MLOPs - These are people take care of the required infra, basically the equivalent of devops. Personally, f…

How well do you know math? Do you really know math?

Re: Ask HN: How to pivot to a Machine Learning engineer?

#25

Post by nottorp »

I'm not "pivoting to a ML engineer" but in the last 2.5 months I've learned to some extent to use public models, use the tools and APIs to train and run them. That was a lot of reading with little code writing.

I didn't pivot into it, that was part of the project (object recognition in a video stream).

It helps if you work with small organizations that don't box you into a role but just give you stuff to do.

Re: Ask HN: How to pivot to a Machine Learning engineer?

#26

Post by jampekka »

vasili111 wrote: post #22
jampekka wrote: post #11

Earlier quoted context omitted.

> Does a career in ML require a strong understanding/knowledge of math or does it require you to be able to solve a lot of math? No. With ANNs, unless you're doing research into totally new ML architectures, you're not gonna do any maths apart from some arithmetic. And even if you do, it's usually quite simple maths, mostly matrix multiplications and simple non-linear transformations of scalars. Nothing even close to…

Just to he sure that I understand the context, are you working as MLE or where from you had that knowledge?

No. I use ML in academic research and teach it. Some come close to what is done in "real-world", e.g. training and evaluating LLMs with real-world datasets.

Re: Ask HN: How to pivot to a Machine Learning engineer?

#27

Post by manishsharan »

nottorp wrote: post #25

I'm not "pivoting to a ML engineer" but in the last 2.5 months I've learned to some extent to use public models, use the tools and APIs to train and run them. That was a lot of reading with little code writing. I didn't pivot into it, that was part of the project (object recognition in a video stream). It helps if you work with small organizations that don't box you into a role but just give you stuff to do.

Could you please share some of the learning resources you used and found useful.

I am overwhelmed by the amount of ML related material out there and am having a hard time finding out material what is worth my time as a software engineer.

Re: Ask HN: How to pivot to a Machine Learning engineer?

#28

Post by ptero »

My 2c: there isn't really a lot of math underneath practicing many ML jobs. But I would read up on basics. Get a basic understanding of optimization and gradient descent. If you are not math inclined, do not bother with untangling chain rules for networks, but play with a few toy, non ML, low dimensional problems. That understanding will help a lot.

And I would look at applying existing models to business problems. That I think has a lot of demand now. Solve a few problems for yourself, make a few blog posts and apply.

Re: Ask HN: How to pivot to a Machine Learning engineer?

#29

Post by Der_Einzige »

treprinum wrote: post #15

Get a PhD in ML from a top school. If you can't, get a MS CS/DS with ML emphasis from a top school, AI grad cert from Stanford at a minimum so that you can understand the latest arxiv papers. If you can't, YOLO and sift through a lot of low-quality articles on the Internet, find the gold nuggets and learn to apply them rapidly and then hope somebody will notice you and hire you. Competition is brutal right now as AI…

Citation needed on "competition is brutal right now".

I'm seeing folks with their first and only workshop paper at an ACL track conference landing 150K offers starting at no-name startups. Some of these folks are not even 20 yet. Workshop papers are considered "easy" to publish, and are held in lower regard compared to main conference publications.

If it's "brutal" to compete against folks like this, I think a lot aren't cut out for this field.

Re: Ask HN: How to pivot to a Machine Learning engineer?

#30

Post by throwup238 »

manishsharan wrote: post #27
nottorp wrote: post #25

I'm not "pivoting to a ML engineer" but in the last 2.5 months I've learned to some extent to use public models, use the tools and APIs to train and run them. That was a lot of reading with little code writing. I didn't pivot into it, that was part of the project (object recognition in a video stream). It helps if you work with small organizations that don't box you into a role but just give you stuff to do.

Could you please share some of the learning resources you used and found useful. I am overwhelmed by the amount of ML related material out there and am having a hard time finding out material what is worth my time as a software engineer.

Karpathy’s zero to hero series on Youtube is considered one of the gold standards for starting out.
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