I've been wanting to do the same, but when I get far into machine/deep learning there is some high level math involved, so I've been taking all the math courses on khan academy for the past couple months https://www.khanacademy.org/. Soon ill finally be done with all the math listed there and move back into learning more about deep/machine learning (even if I stick with programming the math will help in all areas of my life). If anyone has a good math resource I can take after khan academy more geared toward machine learning I am all ears.
Ask HN: How to pivot to a Machine Learning engineer?
41–50 of 57 posts
Re: Ask HN: How to pivot to a Machine Learning engineer?
#42I've had that title.
It helped that I got a PhD in Physics so I knew a lot of math already, also I had been interested in ML for text classification circa 2005 or so. From 2001 to 2010 or so I was busy with a wide range of web applications such as: a social network for a secret society, a blog for a local political party with an integrated telephone response system, an application tracking system for a nanotechnology internship program, etc. There was plenty of brownfield work in there.
I had worked on a series of side projects that got attention and landed me a job as a "relevance architect" (recommender systems and such for a new social media site that softlaunched but didn't get big) and then an ML software engineer where I completed a search engine for patents based on a neural network.
After that I went through a phase of doing random consulting projects but also trying to start my own startup around data engineering problems that didn't get the support I needed. I learned Python because there was huge amounts of Python work in this space. I did a project involving LSTM networks for text around this time.
When I threw in the towel I joined up with a company where I was between the engineering and data science teams, over the course of a year I had figured out most of why our Python systems were not entirely reliable but the company was pivoting a lot and towards the end I was writing more typescript + Scala. Even though I had the physics education I felt my long experience as an applications developer was more important to this role: we had data scientists who were better at creating models than me, but most of them worked in Jupyter Notebooks and didn't have a clear idea of how to take the code they wrote and make it reusable: how to make the "monthly sales report" as opposed to the "April sales report". It was my role to get that discipline in terms of collaboration w/ the data sci's, the process we used, and as embodied in the software I developed.
It was a great experience but also a bit disorganized as we were trying to develop a product but also having to change tack every week to accommodate project work for A-list companies we had as customers. We had a system called "Themis" that was used to build training sets that worked but I disagreed with architecturally (#1 requirement is not sweating it when your A-list company needs something really different). I wrote up a description of a system called "Nemesis" which the company didn't go with but I developed a number of (image|document|comment|job advertisement)-sorters on my own account afterwards that were all called "Nemesis" until I had a vision which lead to YOShInOn and the newer FraXinus which is meant to be an everything sorter. We were working on CNN-text models at this time, BERT came out and we thought it was a big advance but we had no idea how big it would be.
I was burned out from working remote so I went looking for an local job which means doing more ordinary stuff like React programming but I sometimes I get to do some more systems oriented stuff such as writing parsers and code generators and such. At the end of my ML phase I felt that (1) it was more important to have the appropriate labeled data rather than the best models, and (2) UI was the bottleneck for (1) so it was worth "getting gud" at UI so I have. I still have my side projects.
Re: Ask HN: How to pivot to a Machine Learning engineer?
#43Get 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…
So 2014, the year before OpenAI was even founded, was too late to get into the ML space? Very interesting take.
Re: Ask HN: How to pivot to a Machine Learning engineer?
#44Get 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…
So 2014, the year before OpenAI was even founded, was too late to get into the ML space? Very interesting take.
No, but add ~5 years to master it if you are a decent academic performer, then additional few years to learn how to scale it up. Some folks could master it faster but most would likely fail due to the lack of commitment. There is also a bunch of folks that still live in RNN days and cast evil eyes at anyone who uses transformers (hello Jürgen!), so one has to consistently update their knowledge to be relevant (CS25 could help there).
Re: Ask HN: How to pivot to a Machine Learning engineer?
#45Trying to make the same transition right now. I’ve got almost 10 years experience with python and data engineering and I’ve been reading tutorials and playing with projects on the side.
I think I’ve got a grasp of the fundamentals and the ability to learn fast on the job, but every MLE job listing I see wants “4+ years of experience training and deploying models in a production environment” or something (even non-Senior roles!). I’m not sure how to break into it, to acquire a MLE job to get the experience to acquire an MLE job. Does anyone have any advice?
Re: Ask HN: How to pivot to a Machine Learning engineer?
#46I transitioned to what's now referred to as ML engineering 11 years ago, as a programmer I started working on scripts doing all sorts of ETL (in Python - was my intro to the language) and handled datasets saving and loading for training. I managed models serving (we were using Theano at the time) in prod via REST APIs. Also worked (and still do) on writing model architectures with DL experts and I can say I still don…
I'm looking into learning more about ML and how I can create my own models. I enjoy math, but when I read research papers the formulas sound like gibberish. I also like the idea of Kaggle competitions, do you think I need to have research-level math understanding of ML models to do well on Kaggle?
Yes, I believe you do need to have a good math understanding to understand why a model is behaving the way it is and develop intuition into where the problem might be but that comes with experience. If you're looking into ML research then absolutely you need to learn the math but if you intend to support ML as a programmer then there's plenty of space for you to do so
Re: Ask HN: How to pivot to a Machine Learning engineer?
#47Earlier quoted context omitted.
There aren't that many folks who publish even workshop papers. Most folks are scared of academics and hope to raze their way to ML just with dev skills which is unlikely to work as they won't be able to grasp the concepts they need to implement, especially if they work on anything <2 year old. $150k is also on the low end.
There's plenty of demand for doing ML just by calling OpenAI or similar APIs as more or less total black boxes. Probably moreso than for designing and training your own models. And even then it's mostly taking a pretrained model from huggingface and doing fine-tuning and prompt churn by trial and error. E.g. doing or hosting state-of-the-art LLMs is more or less infeasible for many/most use cases. (Applying LLMs succ…
Those jobs are quickly getting commoditized - you can see it e.g. on TopTal where these types of jobs had $150/h last year and $60/h this year. But jobs like "create a framework for interpretable transformers based on some DeepMind research" are still at $250/h.
Re: Ask HN: How to pivot to a Machine Learning engineer?
#48I actually wrote a blog post about this for experienced software engineers like you who are thinking of transitioning to ML, so I wanted to share it here: https://www.trybackprop.com/blog/2024_06_09_you_dont_need_a_... I write about various engineers who now work at Meta, Google, Amazon, and OpenAI who made the switch. You can see what strategies and tactics they used to do it. 1) It's "wise" if you find during your…
How much math is truly necessary to work as MLE in a company where you do not need to write papers but need to deliver working ML systems?
Re: Ask HN: How to pivot to a Machine Learning engineer?
#49The last year has really intensified how often I see this question here or on reddit or otherwhere in my online travels.
You're describing MLOps.
Why would you want to make this transition if you're not keen on the underlying math? Why not include DevOps or SRE if you're interested in code-based pipelines, deployments, and scaling? How are you going to get into things like drift detection without understanding the math?
(...hoping for an answer that isn't shouted by Rod Tidwell at Jerry Maguire -- but genuinely curious why I'm seeing this so much and trying not to make a cynical assumption :) )
Re: Ask HN: How to pivot to a Machine Learning engineer?
#50Don't. Most of these roles are not ethical.
Lol like ordinary programmers have a such a great ethical reputation... Cambridge Analytica, Ad Platforms, and on and on.