[imiric]

Not only for building user-facing tools; the AI/ML space needs a lot of good engineering practices to function. In many cases, traditional software, infra and DevOps, QA and UI engineers, etc., are as crucial to ML projects as data scientists are.

So I don't think you need to worry about being left behind or that your skills are stagnating if you're not directly developing ML models. There's no existential reason to jump in, unless you're particularly interested in ML.

[alexose]

> So I don't think you need to worry about being left behind or that your skills are stagnating if you're not directly developing ML models.

Aren't ML models kind of the front lines these days? If I'm understanding correctly, it's the models, the training techniques, the curation of data sets-- These are the things that will inform the next generation of products and services.

I agree that there's more to it that just letting ML models loose, but it certainly seems like the core of it.

[imiric]

Depends on what you want to focus on. My point is that there are plenty of roles adjacent to the core of ML that are still needed to make ML function. Think about data storage for models, maintaining CI pipelines for training, UIs for curation and labeling, packaging and deploying models, data version control systems, etc. None of these are tasks data scientists should be concerned about, and viceversa, data science is not something engineers should necessarily be concerned about either. It doesn't make either role superior; they just complement each other well.