A/B Testing for ML Models: Best Practices

You've probably deployed a machine learning model that looked amazing in development - great metrics, clean validation scores, the works. Then production hits and suddenly your stakeholders are asking uncomfortable questions about why conversions dropped 3%.

This gap between offline metrics and real-world performance is exactly why A/B testing has become essential for ML deployments. It's the reality check that tells you whether your model actually moves the needle on business metrics, not just accuracy scores.

Understanding A/B testing for machine learning models

A/B testing for ML isn't fundamentally different from testing a new button color. You're still comparing a control (your current production model) against a challenger (the shiny new model). The difference? Stakes are usually higher and the metrics more complex.

When MLOps Community surveyed practitioners, they found most teams struggle with defining the right success metrics. It's tempting to focus on model accuracy, but what really matters is business impact - revenue, conversion rates, user engagement. Your F1 score might be stellar, but if customers aren't buying more products, who cares?

The mechanics are straightforward:

• Split your traffic randomly between models

• Measure outcomes that actually matter to the business

• Run the test long enough to get meaningful results

• Make a decision based on data, not hunches

Platforms like Wallaroo and Statsig have made this easier with built-in experiment configurations. You can route traffic based on user keys, gradually roll out models, or run more complex multi-variant tests. The tooling has caught up to the need.

The hard part isn't running the test - it's designing it properly and having the discipline to let it finish.

Designing effective A/B tests for machine learning

Start with your Overall Evaluation Criterion (OEC). This fancy term just means picking one metric that captures what success looks like. Netflix famously uses viewing hours, while e-commerce sites might focus on purchase conversion. Pick wrong and you'll optimize for the wrong thing.

Next comes the statistics homework. You need:

1. Significance level (usually 0.05 - the risk of calling a winner when there isn't one)

2. Statistical power (typically 0.8 - the chance of detecting a real difference)

3. Minimum detectable effect (the smallest improvement worth deploying)

These numbers determine your sample size. Too small and you'll miss real improvements. Too large and you're wasting time and potentially losing money on an inferior model.

Seldon's engineering team suggests running A/A tests first - testing your current model against itself. Sounds pointless? It's actually brilliant for catching biases in your testing infrastructure. If your A/A test shows a "winner," something's broken in your setup.

The biggest mistake teams make is peeking at results early. Day one shows the new model crushing it, so why wait? Because early results lie. Random variation looks like signal when sample sizes are small. Set your test duration based on statistical requirements, not impatience.

Advanced techniques: Bayesian A/B testing and multi-armed bandits

Traditional A/B testing has a dirty secret: it's inefficient. Half your users get stuck with the worse model for the entire test duration. Enter two techniques that fix this.

Bayesian A/B testing ditches the rigid hypothesis testing framework. Instead of waiting for a predetermined sample size, you continuously update your belief about which model is better. The math gets a bit hairy, but the intuition is simple - you can make decisions as soon as you're confident enough, not when some arbitrary threshold is met.

Multi-armed bandits (MAB) take this further. Named after casino slot machines, these algorithms dynamically shift traffic toward the winning model during the test. Companies like Google and Microsoft use variations of MAB for their recommendation systems.

The tradeoff? MAB tests are harder to interpret statistically. You're changing the rules mid-game, which makes traditional significance calculations wonky. But if you care more about minimizing regret (users seeing the worse model) than statistical purity, they're powerful tools.

Here's when to use each:

• Traditional A/B: When you need clear statistical guarantees or stakeholder buy-in

• Bayesian: When you want faster decisions and can tolerate some uncertainty

• MAB: When the cost of showing the inferior model is high (think medical recommendations)

Best practices and avoiding pitfalls in A/B testing ML models

Let's talk about what goes wrong. Harvard Business Review's analysis found that most A/B test failures come from human error, not statistical issues.

Common ways teams shoot themselves in the foot:

• Stopping tests when they see the result they want

• Testing on biased samples (only power users, only one geography)

• Ignoring seasonality (launching a retail model test on Black Friday)

• Changing the model mid-test because "just this one bug fix"

The solution? Treat your A/B test like a scientific experiment. Write down your hypothesis, methodology, and success criteria before starting. Then stick to it. No peeking, no tweaking, no "just this once" exceptions.

Statsig's platform includes guardrails that prevent some of these mistakes - automatic checks for statistical significance, alerts for metric movements, and experiment isolation. But tooling only goes so far. Discipline matters more.

Post-launch monitoring is where many teams drop the ball. Your model won the A/B test - great! But models degrade. Data drifts. User behavior changes. Set up continuous monitoring for both model performance and business metrics.

The teams that succeed long-term treat A/B testing as part of a continuous improvement cycle:

1. Deploy with an A/B test

2. Monitor performance post-launch

3. Iterate when metrics slip

4. Test improvements before full rollout

5. Repeat forever

Closing thoughts

A/B testing ML models isn't rocket science, but it does require rigor. The tools have gotten better, the statistical methods more sophisticated, but the fundamentals remain: define clear success metrics, run properly powered tests, and have the discipline to trust the data over your intuition.

If you're looking to dive deeper, check out:

• Statsig's experimentation platform for practical implementation

• Trustworthy Online Controlled Experiments by Kohavi et al. for the statistical deep dive

• Your own A/A test results (seriously, run one - you'll learn a ton)

Hope you find this useful!