> 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

> 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. ________ 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.