Summary
In this chapter, we went over the fundamentals of MLOps governance, detailing its key role in maintaining ML systems’ efficiency, accuracy, and reliability. To emphasize the importance of MLOps governance in real-world scenarios, we explored case studies from various sectors, showcasing how this governance model can dramatically impact the success of AI/ML implementations.
As we dove deeper into the topic, we clarified the core components of MLOps governance – data governance and model governance – offering an overview of their function and necessity within the ML model life cycle. Additionally, we went through some real-world scenarios that effectively underscored the relevance and importance of MLOps governance.
On the technical side, we enumerated and discussed several tools available within Vertex AI that aid in ML solution governance and monitoring. We touched upon the functionalities of Model Registry, Metadata Store, Feature Store, Vertex AI...
