Machine Learning

1 readers

1 users here now

Community Rules:

Be nice. No offensive behavior, insults or attacks: we encourage a diverse community in which members feel safe and have a voice.
Make your post clear and comprehensive: posts that lack insight or effort will be removed. (ex: questions which are easily googled)
Beginner or career related questions go elsewhere. This community is focused in discussion of research and new projects that advance the state-of-the-art.
Limit self-promotion. Comments and posts should be first and foremost about topics of interest to ML observers and practitioners. Limited self-promotion is tolerated, but the sub is not here as merely a source for free advertisement. Such posts will be removed at the discretion of the mods.

founded 1 year ago

MODERATORS

communick@academy.garden

[D] Skill Creep in ML/DL Roles - is the field getting not just more competitive, but more difficult? (alien.top)

submitted 1 year ago by mofoss@alien.top to c/machinelearning@academy.garden

31 comments fedilink hide all child comments

At what point do you think there was an inflection point for technical expertise and credentials requires for mid-top tier ML roles? Or was there never one? To be specific, would knowing simple scikit-learn algorithms, or basics of decision trees/SVM qualify you for full-fledged roles only in the past or does it still today? At what point did FAANGs boldly state: preferred (required) to have publications at top-tier venues (ICLR, ICML, CVPR, NIPS, etc) in their job postings?

I use the word 'creep' in the same context 'power creep' is used in battle animes where the scale of power slowly gets to such an irrationally large scale that anything in the past looks extremely weak.

Back in late 2016 I landed my first ML role at a defense firm (lol) but to be fair had just watched a couple ML courses on YouTube, took maybe 2 ML grad courses, and had an incomplete working knowledge of CNNs. Never used Tensorflow, had some experience with Theano not sure if it's exists anymore.

I'm certain that skill set would be insufficient in the 2023 ML industry. But it begs the question is this skill creep making the job market impenetrable for folks who were already working post 2012-2014.

Neural architectures are becoming increasingly complex. You want to develop a multi-modal architecture for an embodied agent? Well you better know a good mix of DL involving RL+CV+NLP. Improving latency on edge devices - how well do you know your ONNX/TensorRT/CUDA kernels, your classes likely didn't even teach you those. Masters is the new bachelors degree, and that's just to give you a fighting chance.

Yeah not sure if it was after the release of AlexNet in 2012, Tensorflow in 2015, Attention /Transformers in 2017 or now ChatGPT - but the skill creep is definitely creating an increasingly fast and growing technical rigor in the field. Close your eyes for 2 years and your models feel prehistoric and your CUDA, Pytorch, Nvidia Driver, NumPy versions need a fat upgrade.

Thoughts yall?

you are viewing a single comment's thread
view the rest of the comments

[–] velcher@alien.top 1 points 1 year ago

I really don't think there is a power creep. In 2016, knowing just script-kiddy level ML knowledge like scikit-learn would not qualify you for full-fledged ML roles at FAANGs anyways. In general, FAANGs always looked for 2 qualities in ML roles - rigorous and principled mathematical background, and solid SWE skills.

Architectures are becoming simpler - Transformers are being used instead of LSTMs for sequence modeling. Code is becoming easier to run and streamlined. CUDA is much easier to install these days. Jax is basically streamlined tensorflow 1.