Chapter 4. Prototyping
By now, you should finally have enough theoretical background knowledge to get hands-on with some AI use cases. In this chapter, you’ll learn how to use a prototyping technique borrowed from product management to quickly create and put ML use cases into practice and validate your assumptions about feasibility and impact.
Remember, our goal is to find out quickly whether our AI-powered idea creates value and whether we are able to build a first version of the solution without wasting too much time or other resources. This chapter will introduce you not only to the theoretical concepts about prototyping, but also to the concrete tools we are going to use in the examples throughout this book.
What Is a Prototype, and Why Is It Important?
Let’s face the hard truth: most ML projects fail. And that’s not because most projects are underfunded or lack talent (although those are common problems too).
The main reason ML projects fail is because of the incredible uncertainty surrounding them: requirements, solution scope, user acceptance, infrastructure, legal considerations, and most importantly, the quality of the outcome are all very difficult to predict in advance of a new initiative. Especially when it comes to the result, you never really know whether your data has enough signals in it until you go through the process of data collection, preparation, and cleansing and build the actual model.
Many companies start their first AI/ML projects with a lot of enthusiasm. ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access