Summary
We’ve covered a lot in this chapter, but keep in mind that this chapter serves as an introduction to the many terms and areas we will cover throughout the book. A lot of the concepts presented here will be returned to in subsequent chapters for further discussion. It’s almost impossible to overstate that the infrastructure AI/ML will need to be successful because so much of the performance is dependent on how we deliver data and how we manage deployments. We covered the basic definitions of ML and DL, the learning paradigms that both can employ, as well as generative AI. We also covered some of the basics of setting up and maintaining an AI pipeline and included a few examples of how other companies manage this kind of operation.
Building products that leverage AI/ML is an ambitious endeavor, and this first chapter was meant to provide enough of a foundation for the process of setting up an AI program overall so that we can build on the various aspects of...
