Rolf Tesmer explains that machine learning and DevOps aren’t oil and water (or maybe they are and we just need to stir harder):
In talking with various development teams, customers and DevOps engineers, a lot of the potential problems of meshing ML development into an enterprise DevOps process can be boiled down to a few different areas this aims to address…
– ML stack might be different from rest of the application stack
– Testing accuracy of ML model
– ML code is not always version controlled
– Hard to reproduce models (ie explainability)
– Need to re-write featurizing + scoring code into different languages
– Hard to track breaking changes
– Difficult to monitor models & determine when to retrain
So DevOps helps with this, right? Right?
Well er, some of them yes, but not all.
DevOps is not a panacea but it can solve certain types of problems well.