Ask HN: What is your ML stack like?
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How did your team build out AI/ML pipelines and integrated it with your existing codebase? For example, how did your backend team(using Java?) work in sync with data teams (using R or python?) to have minimal rewriting/glue code as possible to deploy models in production. What were your architectural decisions that worked, or didn't?I'm currently working to make an ML model written in R work on our backend system written in Java. After the dust settles I'll be looking for ways to streamline this process.

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