Machine Learning Operations (MLOps)
Build – this involves data prep, feature engineering, model building, and testing.
Manage – after the models are created, they are often put in a model repository that is auditable and under version control to promote reuse throughout the organization.
Deploy – this step involves exporting the model or pipeline, deploying, and integrating it with production business systems and applications.
Monitor – continuous monitoring is required to ensure optimal performance. As data drifts, the model may be retrained or a new model can replace the existing.