Monitoring in the MLOps workflow
We learned about the MLOps workflow in Chapter 1, Fundamentals of MLOps Workflow. As shown in the following diagram, the monitoring block is an integral part of the MLOps workflow for evaluating the ML models' performance in production and measuring the ML system's business value. We can only do both (measure the performance and business value that's been generated by the ML model) if we understand the model's decisions in terms of transparency and explainability (to explain the decisions to stakeholders and customers).
Explainable Monitoring enables both transparency and explainability to govern ML systems in order to drive the best business value:
Figure 11.4 – MLOps workflow – Monitor
In practice, Explainable Monitoring enables us to monitor, analyze, and govern ML system, and it works in a continuous loop with other components in the MLOps workflow. It also empowers humans to engage...
