Chapter 9. Go—AI implementation strategy

 

This chapter covers

  • The trade-offs in building an AI algorithm, buying from a provider, or using open source
  • Developing a Lean AI Strategy to minimize risk
  • Taking advantage of the virtuous cycle of AI
  • Learning valuable lessons from AI failures

The previous two chapters gave you all the building blocks you need to define and implement an AI project. It’s now time to put everything together. In this chapter, you’ll learn how to decide which components to build in-house or buy from a third-party, and how to manage the implementation phase. You’ll also become familiar with the most typical bumps in the road faced by inexperienced AI builders. By the time you reach the end of the chapter, you’ll feel confident enough to kick-start your project tomorrow .

9.1 Buying or building AI

Paraphrasing what a wise man once said, sometimes the best way to successfully complete a project is never to start it in the first place. As an AI evangelist, one of the most impactful decisions you can make is to rely on products and services offered by AI providers and to avoid building some or all of the technology needed for an AI project. This decision applies to all three of the components of the project: data, model, and infrastructure.

9.1.1 The Buy option: Turnkey solutions

9.1.2 The Borrow option: ML platforms

9.1.3 The Build option: Roll up your sleeves

9.2 Using the Lean Strategy

9.2.1 Starting from Buy solutions

9.2.2 Moving up to Borrow solutions

9.2.3 Doing things yourself: Build solutions

9.3 Understanding the virtuous cycle of AI

9.4 Managing AI projects

9.5 When AI fails

9.5.1 Anki

9.5.2 Lighthouse AI

9.5.3 IBM Watson in Oncology

9.5.4 Emotional diary

9.5.5 Angry phone calls

9.5.6 Underperforming sales

Summary