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Deep learning job postings have collapsed in the past six months

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Re: Deep learning job postings have collapsed in the past six months

#101

Post by tajd »

pbourke wrote: post #91
supergeek133 wrote: post #81

I feel like it was also a classic case of running before we could crawl. Jumping from A to Z before we could go from 0 to 1. I work at an Residential IoT company, there are quite a few really valid use cases for Big Data and even ML. (Think about predictive failure). We hired more than one expensive data scientist in the past few years, and had big strategies more than once. But at the end of the day it's still "hard…

Everyone wants to fire up Tensorflow, Keras and PyTorch these days. Fewer people want to work in Airflow and SSIS, spend days tuning ETL, etc. This is the domain of data engineering, which bridges software engineering and data science with a dash of devops. I’ve been working in this field for a couple of years and it’s clear to me that data engineering is a necessary foundation and impact multiplier for data science.

How might you recommend moving into this field more?

Re: Deep learning job postings have collapsed in the past six months

#102

Post by andrewprock »

On the plus side, ML systems have become commoditized to the point that any reasonably skilled software engineer can do the integration. From there, it really comes down to understanding the product domain inside and out.

I have seen so many more projects derailed by a lack of domain knowledge than I have seen for lack of technical understanding in algorithms.

Re: Deep learning job postings have collapsed in the past six months

#103

Post by beambot »

apohn wrote: post #84
mrtksn wrote: post #61

Earlier quoted context omitted.

If you think about it, that's the natural outcome. Why? Because people in corporations don't have the incentive to benefit the business but to progress their careers and that's done through meeting the goals for their position and make their upper ups progress with their careers too. So essentially, you have a system where people spend other people's resources for living and their success is judged by making the chai…

>If you think about it, that's the natural outcome. Why? Because people in corporations don't have the incentive to benefit the business but to progress their careers and that's done through meeting the goals for their position and make their upper ups progress with their careers too. This is one of the reasons I roll my eyes whenever I read something like "McKinsey says 75% of Big Data/AI/Buzzword projects do not de…

Followed immediately by the solution: Hire McKinsey analysts to help you deliver insights -- which may or may not get implemented or deliver the results, but it won't matter because everyone has moved on to the next "project".

Re: Deep learning job postings have collapsed in the past six months

#104

Post by stainforth »

pbourke wrote: post #91
supergeek133 wrote: post #81

I feel like it was also a classic case of running before we could crawl. Jumping from A to Z before we could go from 0 to 1. I work at an Residential IoT company, there are quite a few really valid use cases for Big Data and even ML. (Think about predictive failure). We hired more than one expensive data scientist in the past few years, and had big strategies more than once. But at the end of the day it's still "hard…

Everyone wants to fire up Tensorflow, Keras and PyTorch these days. Fewer people want to work in Airflow and SSIS, spend days tuning ETL, etc. This is the domain of data engineering, which bridges software engineering and data science with a dash of devops. I’ve been working in this field for a couple of years and it’s clear to me that data engineering is a necessary foundation and impact multiplier for data science.

Any formalized paths I could take to enter this field?

Re: Deep learning job postings have collapsed in the past six months

#105

Post by Abishek_Muthian »

eric_b wrote: post #28

I've worked in lots of big corps as a consultant. Every one raced to harness the power of "big data" ~7 years ago. They couldn't hire or spend money fast enough. And for their investment they (mostly) got nothing. The few that managed to bludgeon their map/reduce clusters in to submission and get actionable insights discovered... they paid more to get those insights than they were worth! I think this same thing is ha…

Could it be also because for most companies after large investment in DS/ML/DL, they couldn't create a promising solution because they don't have as much access to the data/hardware/talent as Google/Amazon/MS does? And at the end of the day using just an API from the former gives better ROI?

(or) In simple terms, is profitable commercial Deep Learning just for oligarchies?

Re: Deep learning job postings have collapsed in the past six months

#106

Post by hogFeast »

tachyonbeam wrote: post #88
baron_harkonnen wrote: post #51

Earlier quoted context omitted.

> they paid more to get those insights than they were worth! This understates how awful ML is at many of these companies. I've seen quite a few companies that rushed to hire teams of people with a PhD in anything that barely made it through a DS/ML boot camp. To prove that they're super smart ML researchers without fail these hires rush to deploy a 3+ layer MLP to solve a problem that need at most a simple regression…

I worked in a place full of deep learning PhDs, and you'd have people trying to apply reinforcement learning to problems that had known mathematical solutions, and integer programming problems. I don't think the issue is just that companies hire people who are awful at ML, it's also that people are trying to shoehorn deep learning into everything, even when it currently has nothing to offer and we have better solutio…

This is just my general sense, as a very non-expert with more experience of doing than theory...but the benefit is someone knowing the theory AND being able to translate that into revenue.

I think most people view the hard part as doing the PHd, and so lots of people value that experience, and because they have that experience you have this endowment effect: wow, that PHd was hard, I must do very hard and complex things.

To give you an example: Man Group. They are a huge quant hedge fund, in fact they were one of the first big quant funds. Now, they even have their own program at Oxford University that they hire out of...have you heard of them? Most people haven't. Their performance is mostly terrible, and despite being decades ahead of everyone their returns were never very good (they did well at the start because they had a few exceptional employees, who then went elsewhere...David Harding was one). The issue isn't PHds, they have many of them, the issue is having that knowledge AND being able to convert it.

I think this is really hard to grasp because most people expect problems to yield instantly to ML but, in most cases, they don't and other people have done valuable work with non-ML stuff that should be built on but isn't because domain knowledge or common sense is often lacking.

A similar thing is people who come out of CS, and don't know how to program. They know a bit but they don't know how to use Git, they don't know how to write code others can read, etc.

Re: Deep learning job postings have collapsed in the past six months

#109

Post by microtonal »

bratao wrote: post #37
The_rationalist wrote: post #21

I observe the state of the art on most Nlp tasks since many years: In 2018,2019 there was huge progress made each year on most tasks. 2020,except for a few tasks have mostly stagnated... NLP accuracy is generally not production ready but the pace of progress was quick enough to have huge hopes. The root cause of the evil is: Nobody has build upon the state of the art pre trained language: XLnet while there are hundre…

I do not agree with this. I work heavily with NLP models for production in the Legal domain (where my baseline is where a 8GB 1080 must predict more than 1000 words/sec). This year was when our team glued enough pieces of Deep Learning to outperform our previous statistic/old ML pipeline that was been optimized for years. Little things compound such as optimizers ( Ranger/Adahessian), better RNN ( IndRNN, Linear Tran…

I do not agree with this. I work heavily with NLP models for production in the Legal domain (where my baseline is where a 8GB 1080 must predict more than 1000 words/sec).

What kind of network are you using? I can do near-SoTA multi-task syntax annotation [1] with ~4000 tokens/s (~225 sentences/s) on a CPU with 4 threads using a transformer. Predicting 1000 words/second on a reasonably modern a GPU is easy, even with a relatively deep transformer network.

[1] 8 tasks, including dependency parsing.

Re: Deep learning job postings have collapsed in the past six months

#110

Post by RhysU »

mywittyname wrote: post #87
supergeek133 wrote: post #81

I feel like it was also a classic case of running before we could crawl. Jumping from A to Z before we could go from 0 to 1. I work at an Residential IoT company, there are quite a few really valid use cases for Big Data and even ML. (Think about predictive failure). We hired more than one expensive data scientist in the past few years, and had big strategies more than once. But at the end of the day it's still "hard…

This is absolutely right. And when you think about it, the reason behind has been staring us in the face: people who want to do machine learning approach everything as a machine learning problem. It's really common to see people handwave away the "easy stuff" because they want to get credit for doing the "hard stuff." It's not just the data scientists fault. I once heard our chief data scientist point out that they d…

> Clients are paying for a Ph.D. to solve problems in a Ph.D way.

Ideally, "in a PhD way" is with careful attention to problem framing, understanding prior art, and well-structured research roadmaps.

I worry about PhD graduates who seemingly never spent much time hanging out with postdocs. Advisors teach a lot, but some approach considerations can be gleaned more easily from postdocs gunning for academic posts.

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