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.