Besides reading an actual textbook or learning from an online course like (https://eecs189.org/) which can understandably be somewhat daunting at first, I think this is definitely the next best thing.
On the contrary, I don't see a similar desire to contribute back from Amazon, Apple, Facebook or Netflix (although the last 2 do contribute a ton to OSS).
My response to this is: so what? Of course they're going to encourage new developers to use their products - it's only natural they'd teach their own stuff and not AWS/Azure. Developers aren't really influencers (sadly) in the purchasing for cloud infra so I'm not sure that it's a straight up marketing campaign.
It’t a great hiring strategy, as many researchers prefer to work at a company where they can get recognition from publishing academic papers.
1) Work experience.
2) Educational pedigree.
There's also the idea you should hire a program lead who does have demonstrated experience, and let them do the technical interviews of their underlings...
At the companies I've worked for doing scientific programming work, during my interview I had to give a presentation to the engineering team over lunch concerning a technical problem and take questions. If a candidate was a fresh graduate we had a requirement they have 1) a Master's and 2) they would present on any research papers they had published, UNLESS 3) they had interned at our company and everyone liked them, then they could skip the Master's requirement.
I wonder how academic publishers feel about this growing trend to require not just a degree, but now peer-reviewed, published, research papers to get a job. The sentiment behind it is, of course, if you’re really smart, you would be published, so if we look for people who are published, we know they’re smart. The reality, though, is that once this catches on, journals are going to be flooded (even more than they already are) with desperate attempts to get something, anything, with somebody’s name on it since that’s another checkbox they have to tick before they can eat. Just like what happened with higher education.
Going into more detail isn't something that a non-technical manager type would be able to do.
I don't have any knowledge about AI or how to model problems for recommendation systems, or when to use decision trees, versus something else. Is this a skill that I should be actively investing in to not become a dinosaur?
My worry is that in the next 10 or so years, I don't want to end up as a Cobol developer in the world of today i.e., might have a job and good pay, but not being able to work at the next big company or next big idea. What are your thoughts?
My hypothesis is that ML will follow a similar path. It seems like an exotic skill now, but there's already a mass of undergrads familiar with it from their education. We'll still need experienced practitioners to lead projects and architect systems (like DB admins and architects do!). But in some 10 to 20 years, everyone will use ML where appropriate, get some value from it, and it will have lost its hype. There will be some uniquely new capabilities that ML enables, just like DBs enabled storing state at scale, efficiently and cheaply.
I've come to the conclusion that AI/ML is a winner takes all market, I really don't think we'll see millions of ML/AI "developers" like we've seen C++/php/.NET/Java in the past.
For the next 10 years I'll focus on kuberbetes/some big data/some ML, but even though I regret it, I think the only viable option is to get into management.
Be an expert or go home.
2. Fast AI parts 1 & 2
3. The old Google Machine learning course
But, what next?. From my experience, this doesn't give you enough credibility to get you a job interview at even a small sized firm, let alone Google.
Don't get me wrong, I really appreciate all the fantastic AI learning resources out there. Its incredibly enabling, but I feel like I'm missing the point of this - Is it to enable people to start companies using AI based tech, and grow the google compute based ecosystem? If its to grow the number of AI jobs and eligible people for those jobs, I have doubts whether that's actually working, or am I missing something?
These resources from google and courses like Fast AI are great for getting devs up to speed so they can meaningfully contribute to data science projects - filling that big demand for data + ml literate devs, especially internally. They’re not designed to get people jobs (disclosure, getting people jobs in data science is what we do at thisismetis.com)
If you want to go deeper? The open source data science masters is a good set of resources. The first few sections of Goodfellow’s deep learning book are a great crash course in ML math/stats theory. Introduction to Statistical Learning is a staple in most people’s library. There’s a glut of intro level data science content out there on the internet, but intermediate to advanced stuff usually means putting in serious effort or breaking out your checkbook and going back to school (whether traditional or otherwise).
I returned to grad school for ML two years ago, and even now I still struggle with some ML job interviews when it comes to statistics and theoretical questions that I've studied two years for. One particularly challenging part of ML interview is that it covers much more than a typical CS interviews that I'm used to. I had a ML engineer internship interview with a famous ML company recently, and I was asked about sorting algorithms, hashing algorithms, non-convex optimization techniques, gaussian processes and manually compute the jacobian of a NN for backprop on the spot.
I couldn't imagine reading 3 books on python, and wondering will I get an interview. The question should be, can I write a simple program. Measuring by can I get a job interview is asking the reverse question.
I mean, would you hire you? Can you solve a potential company's problems with your AI toolset.
The problem with the MOOC ecosystem at the moment is there's no clear path forward with them. I'd have imagined the MOOC certifications solving this problem, but I feel networking plays a much bigger role in the job market rather than credibility.
The only exception I see is Udacity, which, by its pricing has created a limited pool of graduates, and therefore are valued much higher
I'm not being facetious, this is my honest advice.
> but I feel like I'm missing the point of this