MLOps and Continuous Deployment

Continuous Training and Continuous Integration are focused on making sure that models are of the highest caliber and that there is a single version of the truth for creating the models. This single version of the truth for the Kubeflow pipeline that drives model creation is based on the Feature Store, the Model Registry, the Container Registry, the Artifact store, and the Machine Learning Metadata. In traditional DevOps, the Continuous Deployment process is pushing the most recent changes from the developer base to production. In MLOps the Continuous Deployment process has the same responsibility as in DevOps. The Machine Learning Metadata Data enables MLOPs to release engineering to better understand what it took to build a model and how the iterations were affected from model to model. This will serve as the basis for “model lineage” tracking, the core to this all being immutable artifacts that are referenced via hermetic build definitions so we can have reproducible and replicable results across environments. The Continuous Deployment process picks up where the Continuous Integration process leaves off and takes the pieces necessary to rebuild the Kubeflow pipeline in any environment from the centralized repository that the Continuous Integration process writes to. Continuous Deployment rounds out these three continuous processes and makes sure that the model is actually pushed to and used in a production inference service.