Serving, monitoring, and maintaining models in production

There is no point in deploying a model or an ML system and not monitoring it. Monitoring performance is one of the most important aspects of an ML system. Monitoring enables us to analyze and map out the business impact an ML system offers to stakeholders in a qualitative and quantitative manner. In order to achieve maximum business impact, users of ML systems need to be served in the most convenient manner. After that, they can consume the ML system and generate value. In previous chapters, we developed and deployed an ML model to predict the weather conditions at a port as part of the business use case that we had been solving for practical implementation. In this chapter, we will revisit the Explainable Monitoring framework that we discussed in Chapter 11, Key Principles for Monitoring Your ML System, and implement it within our business use case. In Figure 12.1, we can see the Explainable Monitoring framework and some of...