So some options are: * Building ML models for traditional applications - forecasting, ranking, recommending, all of that. Data Scientists and ML Engineers haven't gone away. * Working as an "AI Engineer" building on top of existing APIs and models. Very hip, but also in flux - I couldn't tell you whether that role will still be considered rare & valuable in a few years time, or what skills will be core to it. * ML Ops, engineering work for building & serving AI & ML models. It's always good to be selling shovels.
I would try to get into a team you're interested in as a SWE and then upskill or pivot. In my experience this is more effective than trying to completely reskill and sell yourself as an unproven prospect for MLE or AI Scientist work. AI/ML teams still need software, and in fact many of the best researchers are not great software engineers.
At 100x the operating cost.
If you have any kind of scale, using a model that's appropriately sized to your task is going to be better.
I think that's the key also in terms of team size, if you don't have a dedicated team to take care of training / running a model then it makes sense to use an existing API and pay for it until you have enough scale.
Lots of times building the inefficient thing to prove market value is better than building the scalable thing that doesn't solve anyone's problems.
Why is that not the job of a software/product engineer?
> none of the highly effective AI Engineers I named above have done the equivalent work of the Andrew Ng Coursera courses, nor do they know PyTorch, nor do they know the difference between a Data Lake or Data Warehouse
they're explicitly not trained in ML/AI. any software engineer can write a good prompt, call an API, and deploy that on an http server.
why is that not just software/product engineering?
Like any other web dev job there are differences across domains that can make it valuable to hire someone with past experience in your particular industry (in this case LLMs), but the only reason this gets a brand new title and others don't is hype.
It might not even still be a speciality in a few years time. Right now though there's a lot of depth to the field that people who aren't focusing on it are missing out on.
While there are advantages to going "full-stack" in this analogy most people focus on one or another
Foundation you need what, a primer on Linear_Algebra+Calc+Stats+Prob. At a minimum you have to know the language. Then, do you need anything of classical AI? Anything of classical ML? Do you even need to have any knowledge of Deep Learning other than it exists? Or transformers? OR do you just need a bunch of tutorials on how to implement GPT API's, structuring and managing prompts, etc.
I know all this information is out there but has anyone linked it together? If so, is it paywalled? I did a quite search on AI Engineer and before I knew it a site was asking me for 20k+ and 1.5 years of my life. I already have a master's in CS, so I assume I can make faster strides and do it for less. Can anyone advise?
If the former, I suggest digging into things like the excellent Fast AI course: https://course.fast.ai/
If the latter, the (relatively new) keyword you are looking for is likely "AI Engineer" - https://www.latent.space/p/ai-engineer
There's an argument that deep knowledge of how to train models isn't actually that useful when working with generative AI (LLMs etc) - knowing how to train or fine-tune a new model is less useful that developing knowledge of the other weird things you have to figure out about prompting, evals and using these models to build production-quality apps.