1 Setting the stage for offline evaluations

 

This chapter covers

  • An introduction to offline evaluations
  • Exploring the AI development lifecycle
  • Highlighting the impact on A/B testing
  • Using offline evaluations in real world product applications
  • Explaining why offline evaluations are incredibly valuable

Recommender systems. Search algorithms. Language models. Computer vision. Predictive analytics. These AI systems represent the backbone of modern digital products. They each carry their own impact, influence, engineering complexity, training methodology, and of course, evaluation strategy. They can appear anywhere—in a streaming app suggesting your next binge-watch, an e-commerce website ranking search results, a social media feed curating content, a chatbot answering customer questions, or a financial app detecting fraudulent transactions.

1.1 Evaluations are a model’s reality check

1.2 Model product development lifecycle

1.3 What are AI model offline evaluations?

1.3.1 The “offline” in offline evaluations

1.3.2 Offline evaluations for internal tools

1.3.3 Data in its many forms

1.3.4 Offline metric categories

1.3.5 Many metrics to choose from

1.4 The two layers of offline evaluations

1.5 AI v.s heuristics

1.6 Influencing online controlled experiments

1.7 Practical applications of offline evaluations

1.7.1 Offline evaluations for online production observability

1.7.2 Online-offline correlation

1.7.3 Off-policy evaluations

1.8 When not to use offline evaluations

1.8.1 Feedback loop dynamics

1.8.2 Balancing offline and online approaches

1.8.3 UX considerations

1.8.4 When computational resources are severely limited

1.9 Summary