[nik_s]

I'm the CTO at a data science company, and this has been my experience too. I've been lucky enough to have quite a few engineers go from zero practical experience to being able to train and deploy complex ml solutions, and the most successful solutions have always involved a combination of just a couple of tools: - airflow and/or celery for running data extraction and transformation jobs - pandas and numpy for data wrangling - sklearn, xgboost, lightgbm, pytorch or tensorflow for training/inference - flask or Django to serve results

It's a handful of technologies, but they're (generally) mature, battle tested, and well documented.

[softwaredoug]

Generally true. Though I will say that in larger orgs, you will occasionally get someone doing some ML they read a paper on that's not well supported by major tooling. I mean it's the same trend chasing you see in engineering...