Summary
This chapter concludes the third part of this book. It also concludes the most technical part of our journey through the best practices. We’ve learned how to develop ML systems and how to deploy them. These activities are often called AI engineering, which is the term that places the focus on the development of software systems rather than the models themselves. This term also indicates that testing, deploying, and using ML is much more than training, validating, and testing the models.
Naturally, there is even more to this. Just developing and deploying AI software is not enough. We, as software engineers or AI engineers, need to consider the implications of our actions. Therefore, in the next part of this book, we’ll explore the concepts of bias, ethics, and the sustainable use of the fruits of our work – AI software systems.
