Machine learning is still too hard for software engineers

My wife is a researcher at Stanford doing some ML stuff and the constant painpoint isn't the researching of novel models or maths or whatever, it's the IT hell of managing a pipeline and data acquisition. I've helped her introduce things like Docker to the lab which seems to have helped but still - software engineering is a totally different skill that really hinders ML work.

Amazon has some cool interactive articles that make it easy to understand some concepts, eg:

https://mlu-explain.github.io/decision-tree/

This goes a little too far. For traditional ML, sure, you need lots of deep statistical knowledge. But the fact is that deep learning is different: it's mostly a black box. no one understands exactly what they're doing, how they're biased, and how exactly these models understand things differently than humans, whether you have a PhD in statistics or not.

Because of that, doing deep learning consists of a bunch of cobbled together heuristics for getting good results and probing the model to give a human an intuition for whether it's learning correctly. The tricks and tips for steering that black box have mostly been developed in the last decade: it is not a super deep well.

These tricks and heuristics are like the knowledge needed to be a technician in a nuclear facility, not the knowledge needed to build the nuclear facility in the first place. It's not nothing, to be sure, but unless you're a researcher developing new novel architectures, a very shallow understanding of the statistics will go a very long way.

Coming from a background in computational quantum chemistry, it’s interesting to see all of the people who say ML is “easy” after taking a few online courses and reading some books on data science. If it’s so easy, invent AGI then, since that is the holy grail of machine learning.

Most of these people claiming expertise do not have a deep grasp of the mathematical fundamentals required for state of the art research in the field. Can you develop a neural network with features that are invariant to permutational and rotational symmetries? If so, how do you efficiently generate the irreproducible representations of the product of the symmetric and special orthogonal groups for use in a fast Fourier transform? What is minimum description length and why is it so fundamental? How do you solve trust-region problems on Riemannian manifolds? Throwing an off-the-shelf PyTorch library at a problem does not make one an expert in machine learning.

I agree entirely with this.

Software developers HAVE to have an understanding of the subject they're developing for. Computers are not brains, and they are not able to understand the objective or context in which they run.

I could spit out their crappy tagline - "the hardest thing about developing __X__ should be __Y__, but it often isn’t." - for almost any topic.

"The hardest thing about developing an inertial navigation system should be getting clean sensor readings, but it often isn't"

"The hardest thing about developing MITM proxies should be getting certs configured, but it often isn't"

"The hardest thing about developing web extensions should be setting up your manifest file, but it often isn't"

"The hardest thing about web development should be handling https requests, but it often isn't"

"The hardest thing about having a baby should be labor, but it often isn't"

"The hardest thing about making a car should be getting high quality steel, but it often isn't"

ON AND ON.