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Hi, Congratulations on your new role. Are you joining a team, or are you the team? If you're joining a team, then you'll probably use what they're using and learn their tooling before you could endeavor to improve it. You're doing it in a professional context, so it will be Python. Many blog posts and articles on popular medium websites address shiny new things, but most of these posts address one of two scenarios: portfolio/toy projects, a project with one individual working on it, a project with data that fits on disk and RAM, and/or a Kaggle project where a good part of the heavy lifting has been done for you (data acquisition, cleaning, feature engineering, metric identification) which never happens in real life because that's what you're hired for in the first place. A big problem in this field is the fragmented tooling and experience, which means you have to weave tools together, unless the team you're joining has it figured out and have internal tooling dialed in. Python dominates. I'm sure other languages are used at other ML shops (we have used Scala in some of our projects) but I think in your situation, there's no need to complicate things. Then again, that is just an opinion. It is not the right answer. The goal is to deliver value. All the best, |