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.
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.