Even if every data science work is special, a lot can be learned from similar problems solved in the past. In this talk, I will share some specific software design concepts that data scientists can use to build better data products.

I will present the challenges we encountered while migrating an ML model from batch to real-time predictions and how we handled them. In particular, I will focus on the design decisions and open-source tools we built to test the code, data and models as part of the CI/CD pipeline and enable us to ship fast with confidence.