🧪 A/B Testing for ML
Compare and validate ML models with data-driven experiments
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Model Serving Strategies
Introduction to A/B Testing
🎯 Why A/B Test ML Models?
Offline metrics don't always predict real-world performance. A/B testing lets you measure actual impact on user behavior, business metrics, and system performance. It's the gold standard for validating that your new model truly improves outcomes before full rollout.
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Key Insight
A model with better offline accuracy might perform worse on business metrics. Always test in production.
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Real Impact
Measure actual user behavior and business outcomes
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Risk Mitigation
Limit exposure to potential model failures
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Data-Driven
Make decisions based on statistical evidence
🔄 A/B Testing Process
1
Define Hypothesis
New model will increase conversion rate by 10%
2
Design Experiment
Choose metrics, sample size, traffic split
3
Run Experiment
Split traffic and collect data
4
Analyze Results
Calculate significance and make decision
✅ When to A/B Test
- •New model versions
- •Algorithm changes
- •Feature engineering
- •Hyperparameter tuning
⚠️ Considerations
- •Need sufficient traffic
- •Time to significance
- •Seasonal effects
- •Multiple testing issues