Building a machine learning model is an iterative process. Many of the steps needed to build a machine learning model are reiterated and modified until data scientists are satisfied with the model performance. This process requires a great deal of data exploration, visualization and experimentation as each step must be explored, modified and audited independently.
ModelOps covers an end-to-end methodology from business understanding to model consumption in production with several components used in the different stages of the model lifecycle.
This section covers the following topics: