What is A/B Testing for ML?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

It depends on the effect size you need to detect and your baseline metrics. For a 2% lift in conversion rate from a 5% baseline, you need roughly 50,000 observations per variant. Use power analysis calculators to determine exact requirements. Most ML A/B tests need 1-4 weeks of runtime. Under-powered tests are the most common mistake, leading to inconclusive results or false positives that waste engineering resources on models that don't actually improve outcomes.

Randomize at the user level, not the request level, to prevent the same user from seeing different model versions. Account for novelty effects by running tests for at least 2 full business cycles. Don't peek at results before reaching statistical significance. Use sequential testing methods if you must monitor results early. Control for network effects in recommendation systems where one user's experience affects another's. Most failed A/B tests fail due to methodology, not model quality.

Test changes that affect user-facing behavior like new recommendation algorithms, updated ranking models, or pricing changes. Skip A/B tests for infrastructure improvements, latency optimizations, and internal model refactoring where metrics can be validated offline. A practical approach is to A/B test major model architecture changes and batch-validate minor updates through shadow deployments. Over-testing slows deployment velocity without proportional quality improvement.

It depends on the effect size you need to detect and your baseline metrics. For a 2% lift in conversion rate from a 5% baseline, you need roughly 50,000 observations per variant. Use power analysis calculators to determine exact requirements. Most ML A/B tests need 1-4 weeks of runtime. Under-powered tests are the most common mistake, leading to inconclusive results or false positives that waste engineering resources on models that don't actually improve outcomes.

Randomize at the user level, not the request level, to prevent the same user from seeing different model versions. Account for novelty effects by running tests for at least 2 full business cycles. Don't peek at results before reaching statistical significance. Use sequential testing methods if you must monitor results early. Control for network effects in recommendation systems where one user's experience affects another's. Most failed A/B tests fail due to methodology, not model quality.

Test changes that affect user-facing behavior like new recommendation algorithms, updated ranking models, or pricing changes. Skip A/B tests for infrastructure improvements, latency optimizations, and internal model refactoring where metrics can be validated offline. A practical approach is to A/B test major model architecture changes and batch-validate minor updates through shadow deployments. Over-testing slows deployment velocity without proportional quality improvement.

It depends on the effect size you need to detect and your baseline metrics. For a 2% lift in conversion rate from a 5% baseline, you need roughly 50,000 observations per variant. Use power analysis calculators to determine exact requirements. Most ML A/B tests need 1-4 weeks of runtime. Under-powered tests are the most common mistake, leading to inconclusive results or false positives that waste engineering resources on models that don't actually improve outcomes.

Randomize at the user level, not the request level, to prevent the same user from seeing different model versions. Account for novelty effects by running tests for at least 2 full business cycles. Don't peek at results before reaching statistical significance. Use sequential testing methods if you must monitor results early. Control for network effects in recommendation systems where one user's experience affects another's. Most failed A/B tests fail due to methodology, not model quality.

Test changes that affect user-facing behavior like new recommendation algorithms, updated ranking models, or pricing changes. Skip A/B tests for infrastructure improvements, latency optimizations, and internal model refactoring where metrics can be validated offline. A practical approach is to A/B test major model architecture changes and batch-validate minor updates through shadow deployments. Over-testing slows deployment velocity without proportional quality improvement.