Summary

In this chapter, we talked about different components of a machine learning life cycle, from data collection and selection to model training and evaluation and, finally, model deployment and monitoring. We also showed how modularizing the data processing, modeling, and deployment aspects of the machine learning life cycle helps in identifying opportunities for improving machine learning models.

In the next chapter, you will learn about concepts beyond improving the performance of machine learning models, such as impartial modeling and fairness, accountability, and transparency toward achieving responsible AI systems.