Architecting Data and Machine Learning Platforms

Chapter 10. AI Application Architecture

In this chapter, you will learn the high-level decisions you should make on architecture and frameworks when building an AI and ML application. We start by considering what kinds of problems AI/ML is good at addressing and how to develop and deploy AI responsibly. Once you have decided that ML is a good fit for a problem, you will have to move on to deciding the enterprise approach you take: do you buy, adapt, or build? We look at examples of each of these scenarios and the considerations if you choose to adopt each of these approaches. If you are building, there are several choices of architectures, and the choice depends on the type of problem being solved.

This chapter covers AI architecture considerations and decision criteria at the application level. The platform on which data scientists and ML engineers will develop and deploy these applications is covered in Chapter 11. Do not skip this chapter and dive straight into the technical details in the next chapter—as a cloud architect, you will need to advise every application team on making the right decision regarding buy, adapt, or build and the choice of AI architecture for each application that they build on your platform.

Note

The purpose of this chapter and the next is to show you how to architect an ML platform using cloud technologies. Just as we did not cover SQL in the chapter on data warehousing, we do not cover TensorFlow in these chapters. If you wish to learn how to do ML, ...