I am an experienced ML manager in a large ecommerce company, and I mostly agree with you, and I can’t wait for this to happen - and I think people just entering college or grad school for ML should not fear it. It’s a good thing.
Right now, there is so much misunderstanding about what ML is, what resources it needs, and how it works that the corporate environment is very stressful.
ML jobs are well paid, but they are NOT fun. No one understands ML devops & the infra needs to enable tight experimentation loops. Existing observability and telemetry systems are wildly bad for model training, reproducibility or any form of online or semi-online learning. As an ML engineer you’ll have to take on huge workloads of devops, infra, tooling, data munging. I’ve seen more than a few brilliant ML engineers burnout and quit because of this.
As ML becomes better understood as a boring technology, and decisions around ML projects, team structure and especially ops support start to get more standardized, I think this will get better.
The pivot you mention means a thinning out of the headcount on the pure ML research side. But it also means opening up more positions in ML engineering, infra & devops.
If people choose their specialization appropriately and remain open to being less on the research side of this, then I think there will continue to be lots of opportunities for high-paying jobs, and people will know their required responsibilities more unambiguously and probably will be happier, rather than dredging through the endless series of bait and switch jobs that exist today, promising a focus on ML research but typically forcing you more into ML devops & data platform management.
> ML jobs are well paid, but they are NOT fun. No one understands ML devops & the infra needs to enable tight experimentation loops. Existing observability and telemetry systems are wildly bad for model training, reproducibility or any form of online or semi-online learning. As an ML engineer you’ll have to take on huge workloads of devops, infra, tooling, data munging. I’ve seen more than a few brilliant ML engineers burnout and quit because of this.
Can I cry? I feel so understood right now.
I love my job in ML, the subject matter is fun, but there is so such a huge burden of expectations on a team's titular data scientist. It is exciting in a 'mid 90s during the web revolution' sort of wild-west way, but you also have the cynicism of the mature Software field. A good ML engineer is worth their weight in gold.
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I also wrote this in a pseudo-fictional dystopian sense. A 'If I was an ML pessimist' take on the the state of things.
The other comments made to the parent I originally posted, are great counter arguments. (2012-14: Alexnet, 14-16: Deep LSTMs, 16-18: Resnet,M-RCNN,Yolo 18-20: Tranformers, 2020+: Alphafold,GPT3,CLIP, et al.) Deep learning has been improving pretty linearly over the last decade. If I was looking at it in a naively statistical sense, then ML will actually be able to match the rising supply of ML scientists with a rising demand. That's the optimistic take though. In that case it will actually feel like being a programmer in the 90s, in that a couple pivots can propel you to multi millionaire.
Right now, there is so much misunderstanding about what ML is, what resources it needs, and how it works that the corporate environment is very stressful.
ML jobs are well paid, but they are NOT fun. No one understands ML devops & the infra needs to enable tight experimentation loops. Existing observability and telemetry systems are wildly bad for model training, reproducibility or any form of online or semi-online learning. As an ML engineer you’ll have to take on huge workloads of devops, infra, tooling, data munging. I’ve seen more than a few brilliant ML engineers burnout and quit because of this.
As ML becomes better understood as a boring technology, and decisions around ML projects, team structure and especially ops support start to get more standardized, I think this will get better.
The pivot you mention means a thinning out of the headcount on the pure ML research side. But it also means opening up more positions in ML engineering, infra & devops.
If people choose their specialization appropriately and remain open to being less on the research side of this, then I think there will continue to be lots of opportunities for high-paying jobs, and people will know their required responsibilities more unambiguously and probably will be happier, rather than dredging through the endless series of bait and switch jobs that exist today, promising a focus on ML research but typically forcing you more into ML devops & data platform management.