In the high-stakes world of machine learning deployment, launching a new model is like piloting a spacecraft - every decision matters, and there’s no room for blind leaps of faith. Enter A/B testing, the mission control center of model deployment that transforms uncertainty into calculated progress. Think of A/B testing as nature’s own evolutionary experiment, but accelerated and controlled. Just as organisms adapt through natural selection, models prove their worth through careful comparison. When deploying a new model, we don’t need to make an all-or-nothing decision. Instead, we create a controlled environment where both the existing champion (Control) and the ambitious challenger (Variant) can demonstrate their capabilities in real-world conditions.

In this series, we will look into the essential components of A/B testing for machine learning models, exploring how to design robust experiments, measure statistical significance, and make data-driven decisions about model deployment. In this first part, we cover the foundations of A/B testing and how to design your experiments.

This is Part 1 of a 3-part series on A/B Testing:

What is A/B Testing?

A/B testing, also known as split testing or bucket testing, is a randomized experimentation method where two versions of a model are compared by exposing them to different segments of users/traffic simultaneously. The ‘A’ version is typically the current production model (control group), while ‘B’ is the new variant being tested.

Why A/B Testing Matters in Machine Learning

For machine learning deployments, A/B testing provides statistical rigor in comparing model performance. It helps teams:

  • Validate model improvements with statistical confidence
  • Measure real-world impact on business metrics
  • Detect potential negative effects before full deployment
  • Make data-driven decisions about model releases

Key Components of A/B Testing

User Segmentation and Traffic Allocation

Let’s examine a practical example with 1000 users using a 90-10 split:

  • Segment 1 (900 users) → only see Model A (Control)
  • Segment 2 (100 users) → only see Model B (Variant)

Core Testing Principles

Each A/B test must maintain these fundamental principles for valid results:

  1. Exclusive Model Assignment
    • Users only interact with one model (A or B). No crossover between test groups.
    • Consistent model exposure throughout the test duration.
  2. User Consistency
    • If User X is assigned to Model A on day 1, they continue with Model A throughout.
    • This stability ensures clean, uncontaminated results, clear attribution of outcomes, and a consistent user experience.
  3. Sufficient Sample Size
    • Larger user bases (e.g., 1000 users) are preferable to smaller ones (e.g., 50 users).
    • Benefits of larger sample sizes:
      • Higher statistical confidence
      • Better user representation
      • More reliable performance metrics
      • Ability to detect subtle model differences

Common Traffic Split Patterns

Different scenarios call for different traffic allocation strategies:

  1. Conservative Launch (New Model Testing)
    • Control (A): 90% (900 users)
    • Variant (B): 10% (100 users)
    • Best for: Initial testing of new models with unknown performance.
  2. Equal Distribution
    • Control (A): 50% (500 users)
    • Variant (B): 50% (500 users)
    • Best for: Comparing well-tested models with similar expected performance.
  3. Multi-variant Testing
    • Control (A): 70% (700 users)
    • Variant B: 15% (150 users)
    • Variant C: 15% (150 users)
    • Best for: Testing multiple model iterations simultaneously.

This is Part 1 of a 3-part series on A/B Testing:

Useful Resources

What is A/B testing?