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Comment by master_yoda_1 | Hacker News Reader

What no deep learning, and nobody is claiming to solve all of your ranking problem in 5 lines of code ;) do we really need this course if we have fast ai ?

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You seem to have missed the entire point of the hundreds of hours of lessons provided by fast.ai, as a service to the community(+), which is this: to explain, and be able to re-implement from scratch, all the things that make those 5 lines of code possible.

Some of that includes coverage of the probabilistic data structures and algorithms that are at the heart of the MMDS course. Along with computational linear algebra, analysis of the details of floating point representation, discussion of C/C++ interop, matrix calculus, parallel processing, Python accelerators like cython and numba, functional and object oriented programming, notation, and a lot more.

Being able to solve challenging problems in 5 lines of code is much harder than solving the same problem in hundreds of lines of code.

(+) fast.ai makes no money from any course - there are no ads, and everything is free. Why are some people so keen to stamp on those who volunteer their time to help others? Open source software development suffers the same problem.

For whatever it’s worth fast.ai is my favourite MOOC and the most impressive set of videos I’ve seen shared freely on the internet. Thankyou for everything you do!

I have to give a small wry moue of amusement at "fast.ai" -- since an `ai` is a type of sloth.

I think the ML hype is actually slowing down. I've been interviewing for ML positions as a new grad and a number of companies have told me they have an excess of data scientists who can train the models, but a dearth of engineers who can actually scale the models up to production. Friends at FAANGs have similar stories.

My personal experience is that most of the models that data scientists are creating will never be scalable.

Thanks for sharing! As a new grad also looking for work, maybe I'll apply to more SE jobs, fewer data science-focused jobs lol.

TBH applying for jobs is scary asf.

Maybe it's just because I've been in industry for 10+ years now, but how are you qualified as a new grad to apply for both?

Double major statistics + cs? masters statistics? Seems those would potentially qualify you if you are a solid candidate?

Not sure why you've been downvoted--it's really tough to know what skills will be marketable in 4+ years when choosing what to concentrate on in college.

In my schooling, I really optimized for the math+stats background, since I enjoyed it and thought it would help me stand out. I even took a short detour into a machine learning PhD before deciding academia isn't for me and leaving with an MS. Now I'm on the job market, and although I have modest coding/engineering skills and a willingness to learn, it's tough to find a company willing to take the risk. Guess I min/maxed a little too much.

Best of luck in your search!

my n=1 exp is that there are tons of openings for ML engineers but they are very wary of hiring them

The goal of a data scientist is not to train the model, but to find useful signals to feed the model (separate the signal from the noise).

And on the front end, tie the model prediction to the business outcome and back.

The rest can or soon to be automated away.

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