2 Anatomy of an offline evaluation

 

This chapter covers

  • Illustrating the anatomy of an offline evaluation
  • Detailing the many faces of offline evaluations
  • Defining the core data as input for offline evaluations
  • Illustrating common pitfalls of offline evaluations in real-world settings
  • Highlighting key engineering concept to scale offline evaluations

Effective offline evaluations serve as a pre-flight checklist, methodically testing how models respond to various conditions, edge cases, and user behaviors. This disciplined approach not only improves performance before launch but provides the confidence needed when asked that all-too-familiar question, especially in a faster pace environment: 'Is this ready for our users?’

As machine learning practitioners, we need to introduce new model versions into the product responsibly. We must ensure that the models don't cause harm or reinforce bias. Offline evaluations represent one of the key tools in our toolbox for fulfilling this responsibility. They function as the critical bridge between theory and practice, providing an early reality check of how a model might perform before it's deployed in a live environment. Consider offline evaluations as your opportunity to identify any structural weaknesses before users are invited to experience the product, somewhat similar to inspecting a building's foundation for cracks before opening the doors to visitors.

2.1 The many faces of offline evaluations

2.1.1 Performance evaluations

2.1.2 Diagnostic evaluations

2.1.3 Simulation-based evaluations

2.1.4 Combining evaluations to bridge the gap

2.1.5 How evaluation feeds development cycles

2.2 Anatomy of an offline evaluation

2.2.1 Data as input

2.2.2 Designing the offline evaluation

2.2.3 Metrics as output

2.2.4 Movie recommendation example

2.3 Common pitfalls of offline evaluations in real world settings

2.3.1 Trust is sometimes lacking

2.3.2 Limited guidance on acceptable outcomes

2.3.3 Friction with production systems

2.3.4 Evaluations that don’t generalize

2.3.5 Overfitting historical data

2.3.6 Difficulty in interpreting metrics

2.4 Engineering considerations

2.4.1 Scaling offline evaluations across teams

2.5 Summary