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Machine learning is still too hard for software engineers

nyckel.com

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Re: Machine learning is still too hard for software engineers

#31

Post by alexcnwy »

swframe2 wrote: post #28

In general, getting a phd is the best way to go but it is not the only way. "The AI Epiphany" channel by Aleksa Gordić is worth watching. Check out his origin story: https://www.youtube.com/watch?v=SgaN-4po_cA He works at DeepMind. He is self taught; without a phd.

Aleksa is great.

Also check out Jeremy Howard from fast.ai - also no PhD but amazing teacher and contributes actively to research.

Chris Olah (Google Brain, OpenAI, etc) go to university at all.

PhD definitely not required.

Re: Machine learning is still too hard for software engineers

#32

Post by glouwbug »

fxtentacle wrote: post #29

"Machine Learning is too difficult" ... says company selling "ML platform [which] can be used by anyone and it only takes minutes to train your first model." No, actually, you're just being dishonest. Even if you hide TensorFlow and the keras models behind a nice GUI, people still need that mathematics knowledge to succeed. And yes, pre-training is great. But you need a shitload of stochastic analysis to make sure th…

Resume driven development is real. Who wants a crud app on their resume when they can have a crud + ML app on their resume? I remember back in like 2016 recruiters devoured anyone with the slightest bit of ML experience on their resumes: it fed back into the ego of developers, and suddenly everyone was an ML expert who could do no better than load a JSON of data and import keras. What a strange trip that time was

Re: Machine learning is still too hard for software engineers

#33

Post by rememberlenny »

This is timely.

I just compiled a few resources I found useful on groking computer vision and deep learning.

Short list I’ve enjoyed recently:

- PyImageSearch blog: https://pyimagesearch.com/blog/

- Fastai library and course: https://www.fast.ai/

- Yan LeCun’s 2021 Spring NYU class: https://m.youtube.com/playlist?list=PLLHTzKZzVU9e6xUfG10TkTW...

Re: Machine learning is still too hard for software engineers

#34

Post by saintarian »

Author of the blog post here - it's very cool to see this on HN!

I wrote this as someone who considers himself a half-decent software engineer trying to use ML for a side project and feeling frustrated by all the effort and "accidental complexity" involved. Why focus on software engineers and ML in this post/rant/company? Because "software is eating the world" and having ML be more accessible to software engineers will broaden the range of problems they can solve.

Thanks for all the comments - I acknowledge all/most of the criticisms as valid. A SaaS/AutoML solution won't work for everyone and definitely not for every problem, and it won't be the only answer to making ML more approachable.

Re: Machine learning is still too hard for software engineers

#37

Post by surrTurr »

Nice read. However, it seems a bit fishy that the website publishing this article is a company offering ML as a service. They as a company directly profit from people not wanting to learn ML themselves. I‘m not saying they wrote this article to increase sales, but it‘s a thing to keep in mind.

Re: Machine learning is still too hard for software engineers

#39

Post by jstx1 »

uoaei wrote: post #27
jstx1 wrote: post #25

Earlier quoted context omitted.

> machine learning and software engineering are different disciplines entirely I don't think so. Or more precisely, they might look different academically but you need to have both as a skill to build something useful.

In-built is the assumption that one person must have both competencies. This may be true for cash-strapped startups but this hardly plays well as general advice.

It's not an assumption, that's my main point - you need both skills in the same person. And it's true everywhere including in the biggest companies.

Re: Machine learning is still too hard for software engineers

#40

Post by fxtentacle »

glouwbug wrote: post #32
fxtentacle wrote: post #29

"Machine Learning is too difficult" ... says company selling "ML platform [which] can be used by anyone and it only takes minutes to train your first model." No, actually, you're just being dishonest. Even if you hide TensorFlow and the keras models behind a nice GUI, people still need that mathematics knowledge to succeed. And yes, pre-training is great. But you need a shitload of stochastic analysis to make sure th…

Resume driven development is real. Who wants a crud app on their resume when they can have a crud + ML app on their resume? I remember back in like 2016 recruiters devoured anyone with the slightest bit of ML experience on their resumes: it fed back into the ego of developers, and suddenly everyone was an ML expert who could do no better than load a JSON of data and import keras. What a strange trip that time was

I feel like we're seeing the next round of that now with all those "build your own advanced Deep Learning AI in 3 simple steps" websites.

Clarifai, Amazon Rekognition, Google AutoML Vision, Nykel (the article here), Amazon Comprehend, Google AutoML Natural Language, MonkeyLearn, Lateral, BigML, Azure ML, Lobe, DataRobot, Rapidminer, Dataiku ...

Did I forget anyone?

EDIT: H2O’s Driverless AI, Floyd, AWS SageMaker, Databricks

EDIT2: Pega Platform, MLFlow, Comet.ml

EDIT3: $SNOW SnowFlake, Spell.ml, Cloudera ML, Alibaba Cloud

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