But then again, a lot of current ML jobs are basically just that - finding some optimal architecture, tune hyperparameters, and bam! You're now a modern "AI" powered company.
Heck, I've encountered plenty of ML jobs that didn't require anything more than familiarity with some known frameworks or libraries, and being able to apply known methods to real-world data / problems.
So I can absolutely understand why people are copy/pasting tutorials or papers, and just doing some slight changes. You're practically miles ahead of the competition, when it comes to the job search.
This is really a myth. Most ML jobs require very detailed understanding of statistics because the devil is in the details.
You need to understand things like multicollinearity, coding biases, missing data techniques, convergence of Markov chains, learning curves, mechanics of various higher order gradient optimization methods, how to really carefully evaluate goodness of fit in a huge range of categories of models (neural nets are the vast minority of all models used in production settings) and a ton more beyond this.
If you read “tensorflow for hackers” and believe you can write production neural nets, it’s a disaster.
I can just tell from my own experience, having interviewed and researched a ton of ML jobs: The majority of ML jobs today seem to be re-branded analytics jobs.
I'd say that a solid 4 in 5 of the jobs I've interviewed for, which were tagged in the ML domain, were just that. Typical [x] analytics jobs which don't really require more than stats 101, and good handling of excel. Basic scripting knowledge were often in the nice-to-know section.
Now, there might be a world difference in the typical ML jobs you see in startup hubs like SF, and the jobs you see elsewhere - but companies are, and have been for almost 10 years, been desperate to get onboard of the hype-train, and have re-banded a lot of jobs to attract those wanting to work with ML or Data Science.
Heck, I've encountered plenty of ML jobs that didn't require anything more than familiarity with some known frameworks or libraries, and being able to apply known methods to real-world data / problems.
So I can absolutely understand why people are copy/pasting tutorials or papers, and just doing some slight changes. You're practically miles ahead of the competition, when it comes to the job search.