DevOps for Data Scientists

Going from development notebook to production implementation is still one of the biggest problems that most data scientists face. Many just don't have experience working in modern software development environments. This post is a good index of things you should at least be familiar with before hoping to get a line of code into production.

I don't 100% agree with all of the recommendations, mostly because there are now opportunities to do things like deploy trained models as API endpoints, plus using AWS Lambda / Google Cloud Functions. Both of these can get around the "our production code is in Java" problem. Even so, this writeup is a good overview.


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