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This is not an instant-gratification with fancy results kind of course. But put in the work, and you will learn some very cool stuff.
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> It’s IT support for the people doing real work. This is an appalling perspective. Good MLE skills seem a lot harder to find that good ML ones.
Someone who can engineer infrastructure, pipelines and fire fight production issues is hard to find, but that’s not the point I was making. My apologies; It is real work; the point I was making is it’s not ML work , any more than writing a yaml file is ML work. If you want to write yaml files, any number of possibilities exist. If you want to work with machine learning, then don’t become a data engineer. The skills a…
A lot of the hard part isn't the model, and especially in a world where bert, xgboost, optuna, pytorch, etc have solved much of the classic problem and forced 'real' DS to specialize on either the business consulting side (not math/engineering) or theory side (barely implemented). The rebrand of 'data analyst' (SQL, powerbi, . ..) to 'data scientist' by even top tech companies underscores this. It's not yet to where web dev has gotten in terms of global $20/hr fiverrr contractors, but already at say $40/hr for someone who can build real production models for more boring scenarios.
The result is the vast bulk of data scientists (phd, self-trained, consulting, ...) we interview are weak engineers, so going from a make-believe notebook to a trickier production scenario requires the data engineer / MLOps / etc to solve a lot that a typical DS doesn't really understand in practice. Scale, latency, distributed systems, testing, etc. Likewise, the part the DS solves has little to do with the latest neuroips paper, and more just about lifecycle tasks like getting better data, which the other folks on the team will often be involved with as well.
So 2 natural high-paying paths here:
data engineer / MLOps -> MLEngineer -> DS
data engineer -> all-in-one data analyst/scientist -> ML/AI data scientist
Stanford CS231n: Convolutional Neural Networks for Visual Recognition [1] The assignments are excellent and will let you implement a deephish network from practically scratch, before diving into modern frameworks and applications. This is not an instant-gratification with fancy results kind of course. But put in the work, and you will learn some very cool stuff. [1]: http://cs231n.stanford.edu/index.html
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In smaller teams/companies one gets to wear multiple hats. However, the term "Data engineer" was specifically created by/for ML folks to get rid of unpleasant repetitive work that has to be done but nobody looks forward to it.
Data engineers exist at organisations without any ML work.
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I agree with your first sentence. I'm not sure I would recommend SCPD. If you want to do real ML work, you pretty much need the PhD. This is a hard thing for people who have 140+ IQs but do poorly for whatever reason with formal education to accept, but it's true. Even if you get one real ML job without a doctoral degree, you won't get a second one. Sure, other 140+ IQs can recognize very smart people with only (or n…
So basically no chance for the OP to ever get to ML as getting into a top 10 ML school for a PhD is a minor miracle, finishing it even bigger and that's just the initial qualification step?
Government may be an option, although it doesn't pay as well as industry and you can end up in a comfortable but stifling role.
In corporate, though? Yeah, you pretty much need to have the appearance of star power, which means degree prestige matters. Whether you're actually any good (and, trust me, there are plenty of mediocre people from top schools) doesn't really matter, because the decision-makers are too stupid to know the difference.
There are ways to play this, though, if you're aiming at industry. Harvard isn't a top-10 CS department, but the people in corporate aren't going to know that, and so "Harvard PhD" is going to make them fellate you just because it's Harvard. That may be an avenue. Or, better yet, get a PhD in something that sounds technical but is easier and less selective.
That said, if your goal is to play the corporate game and make a lot of money, you should probably forget about ML and focus on becoming a manager as quick as possible. If your goal is to do intellectually stimulating work, you should probably not consider corporate, because your work is going to be evaluated by people who are literally 50 IQ points too dumb to do so, and while this noise factor is manipulable rather than truly random, the people who have the skills to do perform said manipulation tend to go into management, not technology.
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It highly depends. I was hired for a small research group that didn't have a product in production. Got hired for programming, was on the table discussing and contributing to research within a couple months without any background in ML.
These types of anecdotes make the actual practice of both ML and AI seem rather, well, less than scientific. There is supposed to be Ph.D. level math behind all of this, yet an amateur with admittedly no ML background is part of the team. In Star Wars , it takes Luke Skywalker years to learn to use a light saber skillfully. Then in The Force Awakens , some ex-Stormtrooper with no training picks up the light saber and…
Creative and critical thinking is not exclusive to people with a PhD. The ability to understand ones strengths and weaknesses is not exclusive to a PhD.
I would never attempt to write or publish a paper without help of somebody with stronger mathematical or statistical knowledge. On the other hand they should not write source code for a paper without consulting somebody with a strong background in sw engineering. You complement each other. Power is in recognizing that.
You would be surprised how many software bugs I have found that invalidated entire (draft) papers. A PhD in ML doesn't save you from that.
My 2c (not exhaustive for what you want to do, probably): 1) Get some statistics/probability basics. It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model). 2) take a look at traditional machine learning approaches. Nowadays you're swamped by DL (a lot of g…
>(Data cleaning and management should also be learned)
There are many students and graduates who either didn't want to do research in the first place or didn't get that research grant or position and looking to get employed in private sector with their degree. Many universities and colleges have now also retooled some of their statistics degrees as dedicated "data science" curriculum who either know basics of ML/DL or have the prerequisite background to learn quickly.
However, in my experience (I am extrapolating from my own past job search experiences) while "understanding theory behind the algorithms" counts still for something, it is much less than one would think. Familiarity with the software technologies and practical implementation is what counts much more. This includes not only "data management", a phrase which makes it sound like the data simply exists somewhere and only needs to be managed (not unlike a Kaggle competition), but also the data pipeline management from generation/collection to analysis and communication of the results, and deploying the software the implements it all, and so on. I suppose (never been on that end of the interview table) given any two candidates to interview, it is very difficult to evaluate how deeply one understands theory of some algorithm compared to other if they both demonstrate some basic understanding (and what is the practical use of possible difference in insight from such differential, anyway?). Likewise, I assume it is somewhat easier to gauge whether someone seems to able start delivering results or contributing to their on-going work quickly if they have the relevant technical skills and/or domain knowledge.