Chapter 11Designing ML Training Pipelines
Delivering business value through machine learning (ML) is not only about building the best ML model for the use case at hand. Delivering this value is also about building an integrated ML system that operates continuously to adapt to changes in the dynamics of the business environment. Such an ML system involves collecting, processing, and managing ML datasets and features; training and evaluating models at scale; serving the model for predictions; monitoring the model performance in production; and tracking model metadata and artifacts.
The challenge with ML systems is that ML code is a small part of the overall ML component. You need to manage the relationship between models, data, and the ML code, as shown in Figure 11.1.
FIGURE 11.1 Relation between model data and ML code for MLOps ...
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