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> What's hard about AI that requires special expertise? AI is ill-defined so the premise of your comment makes it difficult to answer. For small well-known tas
by belval 3y ago
> What's hard about AI that requires special expertise?
AI is ill-defined so the premise of your comment makes it difficult to answer. For small well-known tasks (image classification, object detection, sentiment detection) that is train-once on a single dataset and deploy-once what you are saying is true, but for more complex products there is a lot of arcane knowledge that can go in training/deploying/maintaining a model.
On the training side, you need to be able to define the correct metrics, identify bottlenecks in your dataloader, scale to multiple nodes (which is itself a sub-field because distributing a model is not simple) and run evaluation. Throughout the whole thing you have to implement proper dataset versioning (otherwise your evaluation results won't be comparable) and store it in a way that has enough throughput to not bottleneck your training without bankrupting the company (images and videos are not small).
Finally you have a trained model that needs to be deployed, GPU time is expensive so you need to know about compilation techniques/operator fusing, quantization and you need to be able to scale. The requirements to do that are complex because the input data is not always just text.
So yes all the above (and a lot more) require specific expertise.
- haltist 3y agoHow long would it take for someone to learn all that?
- skirmish 3y agoIMO you can only learn al that by doing a few successful ML projects end-to-end. So, a few years?
- haltist 3y agoNot that long then, especially if someone was motivated enough to complete the projects as quickly as possible.
- iaw 3y agoIs this a catch-22 then or is there a rational course to self-study into the field for those that are competent?
- skirmish 3y agoWell, these were senior level skills, a person who can drive and complete a project. I don't know how you could become senior via self-study and without practical hands-on experience on a project (working with and learning from somebody with experience).
- belval 3y agoAs with most topics in software engineering I'd say you will be have to keep learning as you go. They keep coming out with larger models that require fancier parallelism and faster data pipelines. Nvidia comes out with a new thing to accelerate inference every year. Want to use something else than Nvidia? Now you need to learn TPU, Trainium, Meta Accelerator (whatever its name is).
- rg111 3y ago2-3 years of full time or near full time study. I know cause I did it. And I knew the math beforehand. I was a Physics major in college with a CS minor.