With proper versioning, we can combine model predictions and the corresponding input data with model versions and trained data. With this kind of grouping, we can eventually detect data drifts and model miss performances. When it comes to implementation of the ML model, once we have set up the right versioning components and deployment scripts, then we can periodically run (batch) jobs that parse our predictions and analyze their quality.

Read more

I worked as a Solution Architect in building an MLOps platform that is offered as a service and aims to be the backbone for most of the ML operations running