A Practical Guide to A/B Testing Machine Learning in Production

Even after deployment, machine learning models need constant tuning to maintain accuracy and performance. This means that once you deploy a model, you must track its performance over time, looking for any signs of decay. When a model produces less-than-desirable results, you need to figure out why. A/B testing is one of the most effective ways to do this. This post will explore how this approach can help you achieve your goals, like confidently deploying machine learning models that perform reliably in the real world, backed by data-driven validation and measurable business impact. One valuable tool to help you achieve your objectives is Inference's AI inference APIs. They let you easily run A/B tests on machine learning models, so you can validate your results and ensure that any changes you make to your model will lead to improved performance. It also helps you in Monitoring ML Models in Production.

What is A/B Testing in Machine Learning?

A/B testing compares two or more versions to evaluate which works better and whether the difference is statistically significant. In machine learning, A/B testing validates changes to predictive models by observing their performance in real-world scenarios.

Using A/B testing, machine learning models may be evaluated and improved. This approach can be used to determine whether a new model is better than an existing one.

How A/B Testing Works

Using A/B testing, users are divided into two (or more) groups, each exposed to different model versions. The model versions may be identical, except for a few minor changes, or differ significantly.

By monitoring each group's key performance metrics (KPIs), data scientists can determine which model performs better and if any observed differences are statistically significant.

Evaluating Machine Learning Models Before A/B Testing

Before diving into the A/B testing process, it’s essential to understand how models are evaluated before and after deployment. Two key approaches are used for this: offline evaluation and online evaluation.

Offline Evaluation

Offline evaluation is the model’s first test, assessing its performance on historical data in a controlled environment. This involves splitting a dataset into training, validation, and test sets, allowing the model’s predictive power to be measured using metrics like accuracy, precision, and recall.

In this phase, the model is tested on unseen data (test set) to ensure it can generalize beyond the training data, helping data scientists improve the model before real-world use.

Online Evaluation

In contrast, online evaluation occurs in real time after the model is deployed. Here, the model interacts with live users, offering valuable insights into its performance in unpredictable, real-world scenarios.

This method often reveals challenges like data drift (when input data changes over time) or evolving user behavior. Metrics like latency, user engagement, and feedback become critical for understanding real-world performance.

A/B Testing for Machine Learning Models

Let’s consider GPT-2, a large language model, to see these evaluations in action.

Offline Evaluation for GPT-2

When developing GPT-2, offline evaluation measures its ability to generate coherent and contextually accurate responses. Key metrics include:

• Perplexity: This measures how uncertain the model is in predicting the next word in a sequence. Lower perplexity suggests the model understands language patterns better.
• BLEU score: Used to measure the overlap between the model’s generated text and a reference, especially in tasks like translation or summarization.

During this phase, GPT-2 might be tested on factual accuracy, response relevance, and language fluency. For example, it should correctly answer factual questions like, “What is the capital of France?” (Answer: “Paris”) and provide grammatically correct responses that are relevant to the context.

Online Evaluation for GPT-2

When GPT-2 is deployed, online evaluation helps monitor its performance with live user queries. Key metrics include:

• User engagement: Do users engage in multi-turn conversations or drop off quickly?
• Response satisfaction: Are users satisfied with the responses, as ratings or feedback indicate?
• Response accuracy: Does GPT-2 provide real-time, accurate answers to factual queries like, “What’s the weather in New York?”
• Latency: How quickly does the model generate responses? Faster response times improve user experience.
• Personalization: Does the model provide relevant, personalized recommendations based on user behavior?

By leveraging both offline and online evaluations, GPT-2 can be tested thoroughly in controlled conditions and then continuously improved based on real-world performance.

Why A/B Testing Is Crucial for Evaluating ML Models

Offline and online evaluations offer valuable insights into how well a model performs in controlled and real-world environments. The next step is to ensure that your model works well in theory and delivers meaningful improvements in practice. This is where A/B testing becomes crucial. A/B testing is crucial in ML model design. It systematically compares a new model’s performance against the existing one using real-world data. This provides actionable insights into how the model behaves in production, guiding optimizations beyond offline testing.

Validating Changes in Real-World Scenarios

Offline evaluations often don’t capture real-world dynamics like user behavior changes or data drift. A/B testing enables models to be tested on live traffic, ensuring performance holds in production environments.

Reducing Deployment Risks

Gradually introducing a new model to a subset of users minimizes risks, identifying performance issues early while preventing potential negative impacts on user experience or business outcomes.

Data-Driven Decisions

A/B testing provides quantifiable feedback on key metrics, such as:

• Accuracy
• User engagement
• Business KPIs (e.g., conversion rates)

Measuring Statistical Significance

A/B testing helps confirm that observed improvements are not due to chance, ensuring consistent performance improvements.

Example Workflow for A/B Testing in Model Improvement

When To A/B Test Machine Learning Models

After establishing the workflow for A/B testing, ask yourself when you should implement it. A/B testing isn’t always necessary, especially when updates are minor or purely technical.

For example, offline evaluation may be sufficient if you’re making backend improvements, such as optimizing performance or retraining a model on updated data without changing the output. A/B testing might add unnecessary overhead without yielding valuable insights in these cases.

Key Scenarios for A/B Testing in ML

A/B testing becomes essential in specific scenarios. When multiple model versions are used and their performance is compared in real-world conditions, A/B testing provides a controlled way to assess which model performs better.

It’s beneficial when user behavior is a key factor, such as in recommendation systems or search engines, where even small changes can significantly impact engagement. If your goal is to improve business metrics like click-through rates or revenue, A/B testing also helps quantify the effect of model changes, ensuring they align with broader business objectives.

Making A/B Testing Work in Practice

By understanding when A/B testing is necessary, you can make more informed decisions about when to implement it, ensuring efficient model improvements with real-world benefits.

Now that we’ve explored why A/B testing is crucial and when it’s most effective, the next step is understanding how to execute it to get actionable results.

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Step-by-Step A/B Testing Machine Learning Guide

A/B testing machine learning models relies on statistical tests to verify whether the observed performance difference between two models is significant and not due to chance. Let's dive into the practical side, starting with two commonly used statistical tests, G-Test and Z-Test, which help validate whether the observed differences between your control and treatment groups are meaningful.

G-Test

The G-Test is often used to determine if there's a significant difference in categorical data, like conversion rates or event counts, between two groups. It's beneficial for smaller sample sizes or non-normally distributed data, where traditional methods like the Z-Test may not be ideal.The G-Test compares observed frequencies (e.g., the number of conversions in the control and treatment groups) with expected frequencies, assuming no difference between the groups. The formula for the G-Test statistic is:

Where:

• Oᵢ represents the observed frequencies for each group (e.g., the actual number of conversions in each group)
• Eᵢ represents the expected frequencies under the null hypothesis (i.e., assuming no difference between the groups)

The G-value is compared against the chi-squared distribution to determine if the observed difference is statistically significant. If the p-value is below a certain threshold (e.g., 0.05), we can reject the null hypothesis and conclude that the difference is substantial.

Z-Test

The Z-test is typically used to compare the means of two groups, such as the average click-through rates (CTR) of the control and treatment groups. The Z-test assumes that the data is normally distributed, which is a reasonable assumption when the sample size is large, thanks to the central limit theorem. The Z-Test statistic is calculated as:

Where:

• X̄₁ and X̄₂ are the means of the two groups (e.g., the average CTR of the control and treatment groups)
• σ₁² and σ₂² are the variances of the groups
• n₁ and n₂ are the sample sizes of the groups

The resulting Z-value is then compared against the standard normal distribution to determine the difference's significance. A p-value lower than a set threshold (e.g., 0.05) indicates that the difference between the two groups is statistically significant.

How to Implement A/B Testing in ML

Implementing A/B testing in machine learning requires statistical planning, engineering infrastructure, and cross-functional collaboration. The goal is to assess how different model versions perform under real-world conditions by exposing them to live data and comparing their impact. Here’s a more detailed, expanded walkthrough of the steps involved:

1. Define the Objective

Before writing a single line of code, you need to define what you’re trying to test. This includes stating a clear hypothesis and identifying the exact performance metric(s) that will validate or invalidate that hypothesis.

Ask questions like:

• What change is the new model introducing (e.g., architecture, features, training data)?
• What does success look like (e.g., lower churn, faster inference)?
• Which metric best captures that success (e.g., precision, recall, revenue impact)?

Ensure that these metrics are actionable and measurable in your current infrastructure. Tie each metric directly to business outcomes, such as customer satisfaction, conversion rate, or lifetime value.

Pro tip: Predefine primary and secondary metrics to avoid post hoc bias.

2. Select Models to Test

Choose your control (Model A) and variant (Model B). Make sure both models:

• Use the same data preprocessing pipeline
• Generate predictions on the same input format
• Are wrapped in a consistent API contract if being served via microservices

If the models are too dissimilar in input handling or logic, the A/B test may evaluate architectural differences rather than output quality. Consider A/B/n testing if you want to assess multiple model variants simultaneously. Doing so requires increased traffic and careful traffic splitting to maintain statistical power.

3. Set Up Traffic Splitting Logic

Your A/B test is only as good as your traffic allocation. The most common practice is randomly assigning a fixed percentage of users or requests to each model using a hash-based or deterministic routing mechanism.

Ensure that:

• Users are consistently routed to the same model (session stickiness)
• Traffic splits are isolated by relevant segments (e.g., mobile vs. desktop)
• There’s no data leakage or cross-contamination between groups

You may start with a small rollout (e.g., 90% control, 10% variant) to minimize risk and gradually increase it.

Advanced tip: Use feature flag platforms (e.g., LaunchDarkly, Split.io) to control traffic exposure fine-grainly.

4. Decide How Much Error You Want to Tolerate

Again, you probably want to say “none,” but that isn’t practical. The less error you can tolerate, the more data you need, and in an online setting, the longer you have to run the test. In the classical statistics formulation, an A/B test has the following parameters to describe the error:

α: The Significance or False Positive Rate We Are Willing to Tolerate

Ideally, we want α as small as possible; in practice, α is usually set to 0.05. This means that if we run an A/B test repeatedly, we will incorrectly pick an inferior challenger 5% of the time.

β: The Power or True Positive Rate We Want to Achieve

Ideally, we would like β near 1; in practice, β is usually set to 0.8. This means that if we run an A/B test repeatedly, we will correctly pick a superior challenger 80% of the time.

Note that α and β are discussing incompatible circumstances (that’s why they don’t add up to 1). The first case assumes the challenger is worse, the other case assumes it’s better; finding out which situation we are in is the whole point of an A/B test. There’s one last parameter in an A/B test:

n: The Minimum Sample Size Needed for Statistical Significance

We must examine the minimum number of examples (per model) to ensure our false positive rate α and actual positive rate β thresholds are met. Or as it’s commonly said, “to make sure we achieve statistical significance.”

Note that n is per model. So, if you are routing your customers between A and B with a 50-50 split, you need a total experiment size of 2*n customers. If you are routing 90% of your traffic to A and 10% to B, then B has to see at least n customers (and A will then see around 9*n). So, a 50-50 split is the most efficient, although you may prefer an unbalanced split for other reasons, like safety or stability.

Sample Size and Traffic Split in A/B Testing

To run an A/B test, the experimenter picks α, β, and the minimum effect size δ, then determines n. We won’t go into the formula for calculating n here; so-called power calculators or sample-size calculators exist to do that for you.

Here’s one for rates, from Statsig; it defaults to α = 0.05, β = 0.8, and a split ratio of 50-50. Feel free to play around to understand how big sample sizes have to be in different situations.

5. Deploy Models in Production

Each model must be deployed to enable independent logging, monitoring, and rollback. Containerize each Docker model and orchestrate deployments using Kubernetes or serverless functions (like AWS Lambda or Vertex AI endpoints). Key considerations:

• Consistency in data processing pipelines
• Logging predictions, inputs, and contextual metadata
• Assigning model versions for traceability

You can use an API gateway or reverse proxy to manage traffic routing dynamically.

6. Monitor and Collect Metrics

Effective monitoring is essential for detecting performance differences and operational issues. Set up dashboards for:

• ML metrics:
  • Accuracy
  • Recall
  • AUC
  • Log loss
• System metrics:
  • CPU usage
  • Latency
  • Error rates
• Business metrics:
  • Conversions
  • Click-throughs
  • Revenue per session

Aggregate and segment these metrics by user cohort, geography, or device type to uncover subgroup-level performance variations. Use telemetry tools like Prometheus/Grafana or cloud-native options like:

• SageMaker Model Monitor
• GCP Cloud Monitoring
• Datadog

7. Run the Experiment

Run the experiment for a sufficient time to capture representative user behavior and business cycles. Avoid ending the test early unless the results are statistically extreme. Perform power calculations to determine the required number of users or events to reach a desired confidence level (e.g., 95%) and effect size. Monitoring interim results via rolling averages is often helpful, but avoid concluding until the experiment concludes. Also, avoid peeking at results daily and making reactive changes; this introduces confirmation bias and increases false positives.

8. Analyze Results

After data collection, statistical tests should be applied to determine whether the differences between Model A and Model B are significant.

Methods include:

• T-tests for means
• Chi-square for categorical outcomes
• Mann–Whitney U test for non-normal distributions
• Bayesian methods for probabilistic inference

Calculate:

• P-values
• Confidence intervals
• Uplift in performance (% difference)

Visualization tools (like Tableau, Seaborn, or Matplotlib) are invaluable in communicating these insights to technical and non-technical stakeholders.

9. Decide and Deploy

Once the analysis confirms a statistically significant improvement, you can choose to:

• Promote Model B to full production
• Roll back if it underperforms
• Continue testing with more traffic or a new variant

Integrate this decision process with your MLOps pipeline and CI/CD workflows. Update documentation and model registries, and send automated alerts or dashboards to relevant teams. Also, before full deployment, consider retraining frequency, data drift, and how the new model integrates with downstream systems.

Best Practices

• Keep variants isolated: Avoid shared infrastructure bugs
• Use guardrails: Monitor for model drift, latency spikes, error rates
• Bias detection: Analyze performance across age, gender, or geography
• Automate rollback: Set thresholds to revert changes without delay
• Version everything: Data, features, models, and experiments

Extensions to A/B Testing

The classical (AKA frequentist) statistical approach to A/B testing described above is unintuitive for some people. In particular, note that the definitions of α and β posit that we run the A/B test repeatedly; in actuality, we generally run it only once (for a specific A and B). The Bayesian approach takes the data from a single run as a given, asking, “What OEC (Overall Evaluation Criterion) values are consistent with what I’ve observed?” The general steps for a Bayesian analysis are roughly:

• Specify prior beliefs about possible values of the OEC for the experiment groups. An example prior might be that conversion rates for both groups are different, and both are between 0 and 10%.
• Define a statistical model using a Bayesian analysis tool (i.e., distributional techniques) and flat, uninformative, or equal priors for each group.
• Collect data and update the beliefs on possible values for the OEC parameters as you go. The distributions of possible OEC parameters start out encompassing a wide range of possible values, and as the experiment continues, the distributions tend to narrow and separate (if there is a difference)
• Continue the experiment as long as it seems valuable to refine the estimates of the OEC. The delta effect size can be estimated from the posterior distributions of the effect sizes.

Note that a Bayesian approach to A/B testing does not necessarily make the test any shorter; it simply makes quantifying the uncertainties in the experiment more straightforward, and arguably more intuitive. For a worked example of frequentist and Bayesian approaches to treatment/control experiments (in the context of clinical trials), see this blog post from Win Vector LLC.

Multi-Armed Bandits

If you want to minimize the waiting until the end of an experiment before taking action, consider Multi-Armed Bandit approaches. Multi-armed bandits dynamically adjust the percentage of new requests that go to each option, based on that option’s past performance. Essentially, the better a model performs, the more traffic it gets, but some small traffic still goes to poorly performing models, so the experiment can still collect information about them.

This balances the trade-off between exploitation (extracting maximal value by using models that appear to be the best) and exploration (collecting information about other models, in case they turn out to be better than they currently appear). If a multi-armed bandit experiment is run long enough, it will eventually converge on the best model, if one exists.

Using Multi-Armed Bandit Tests for Efficient Experimentation

Multi-armed bandit tests can be helpful if you can’t run a test long enough to achieve statistical significance; ironically, this situation often occurs when the delta effect size is small, so even if you pick the wrong model, you don’t lose much.

The exploitation-exploration tradeoff means you gain more value during the experiment than running a standard A/B test.

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• Model Versioning
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• Machine Learning Observability

Industry Examples of A/B Testing ML Models

In his Lessons learned from building practical deep learning systems lecture, Xavier Amatriain describes 12 lessons he’s learned from building and deploying deep learning systems in production. Lesson 9 of the talk emphasizes the need to validate machine learning models using online experimentation.

According to Amatriain, positive offline performance indicates the need to perform online tests via A/B tests. To test whether a new model should be deployed, data scientists should.

Measuring Metric Differences and Validating Models Before Deployment

Measure differences in metrics across statistically identical populations that each experience a different algorithm.

Once significant improvements have been observed during online tests, we can roll out new models to the user base. This implies an additional validation step before deploying a model to all users. According to Amatriain, how offline metrics correlate with A/B test results is poorly understood.

Insights from Booking.com

A similar idea is expressed in the 2019 paper 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com. In section 3, entitled Modeling: Offline Model Performance is Just a Health Check, the authors state that:

At Booking.com, we are very concerned with the value of a model to our customers and business. Such value is estimated through Randomized Controlled Trials (RCTs) and specific business metrics like conversion, customer service tickets, or cancellations. An exciting finding is that increasing a model's performance does not necessarily translate to a gain in value.

Understanding the Correlation

We stress that this lack of correlation is not between offline and online performance, but between offline performance gain and business value gain. At the same time we do not want to overstate the generality of this result, the external validity can be easily challenged by noting that these models work in a specific context, for a specific system, they are built in particular ways, they all target the same business metric, and they are all trying to improve it after a previous model already did it.

Nevertheless, we still find the lack of correlation remarkable. A correlation can be observed only where the offline metric is almost precisely the business metric.

Four Factors Explaining the Gap Between Model Performance and Business Value

According to the authors, this phenomenon can be explained by four factors:

• Value Performance Saturation: It’s not possible to continue deriving business value from model improvements indefinitely
• Segment Saturation: Over time, the size of the treatment groups decreases, so it becomes more difficult to detect statistically significant gains in value
• Uncanny Valley effect: Certain users are unsettled by how well models predict their actions as model performance improves over time, negatively affecting the user experience.
• Proxy Over-optimization: Models may over-optimize observable variables that are proxies for specific business objectives.

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