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 experien

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.