Production-Ready A/B/n Testing Framework

Build a statistically rigorous multi-variant testing system with deterministic routing, async logging, and automated analysis

Problem Statement

We Asked NEO to: Build a production-ready A/B/n testing framework that supports multiple model variants simultaneously, implements statistical rigor with ANOVA and pairwise testing, ensures deterministic user routing via MD5 hashing, and provides automated winner recommendations with <1ms logging overhead.

Solution Overview

NEO designed a comprehensive multi-variant experimentation platform with statistical rigor and production-grade performance:

1. Deterministic Router with MD5-based bucketing for consistent user experiences
2. Async Logger with queue-based buffering for non-blocking metric collection
3. Statistical Analysis Suite featuring ANOVA, Chi-Square, and Bonferroni-corrected pairwise tests
4. Automated Reporting with publication-ready visualizations and winner recommendations

The framework enables testing 3+ model versions simultaneously while maintaining <2ms total overhead and rigorous statistical validity.

Workflow / Pipeline

Repository & Artifacts

Generated Artifacts:

• Deterministic routing engine with MD5 hashing
• Async logging system with queue-based buffering
• Statistical testing suite (ANOVA, Chi-Square, pairwise tests)
• Automated visualization generator (matplotlib/seaborn)
• Model registry and prediction interfaces
• FastAPI serving endpoint for production deployment
• YAML-based experiment configuration

Technical Details

• Routing Algorithm: MD5 hashing ensures same user always sees same variant with O(1) lookup
• Traffic Distribution: Configurable splits (e.g., 40%/30%/30%) validated to sum to 1.0
• Async Logging:
  • Queue-based buffering for non-blocking writes
  • Batched I/O reduces storage overhead
  • Graceful shutdown prevents data loss
• Statistical Methods:
  • ANOVA for continuous metrics (latency) across N variants
  • Chi-Square for categorical metrics (conversion rates)
  • Bonferroni correction controls family-wise error rate
  • Cohen’s d for effect size estimation
• Performance:
  • <0.5ms routing overhead per request
  • <1ms async logging overhead
  • 1000+ requests/second throughput
  • ~2s analysis time for 10,000 records

Results

• Routing Accuracy: <10% deviation from target split with 1000+ samples
• Logging Overhead: <1ms per request with async queue insertion
• Statistical Power: Successfully detects latency differences with p-value < 0.001
• Analysis Speed: Complete statistical suite runs in ~2 seconds for 10,000 records
• Throughput: 1000+ requests/second on single core

Example Output

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STATISTICAL TESTS
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One-Way ANOVA for Latency:
  F-Statistic: 59.2124
  P-value: 0.000000
  Significant (α=0.05): True

Pairwise T-Tests with Bonferroni Correction:
  baseline_vs_variant_a:
    P-value: 0.000000
    Mean Difference: 1.94 ms
    Cohen's d: 0.1946
    Significant: True

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FINAL RECOMMENDATION
============================================================

Overall Winner: variant_a

Recommendation:
Variant A has significantly better latency (48.06ms) with 3.88% 
improvement over baseline

Best Practices & Lessons Learned

• Use MD5 hashing for deterministic routing instead of random assignment to ensure consistent user experience
• Implement async logging with queue buffering to avoid blocking prediction requests
• Apply Bonferroni correction when running multiple pairwise tests to control false positive rate
• Calculate effect sizes (Cohen’s d) alongside p-values to assess practical significance
• Warm up all models before accepting production traffic
• Validate traffic splits sum to 1.0 at configuration load time
• Log latency at each pipeline stage to identify bottlenecks
• Generate both statistical metrics and visualizations for stakeholder communication

Next Steps

• Implement Bayesian A/B testing with Thompson Sampling for dynamic traffic allocation
• Add sequential testing with alpha spending functions for early stopping
• Extend to multi-armed bandit algorithms for exploration-exploitation
• Build real-time dashboard with WebSocket updates
• Add database integration (PostgreSQL/MongoDB) for production-scale logging
• Implement automatic experiment stopping rules based on statistical significance
• Add Slack/email notifications for significant results
• Build cohort analysis and segmentation capabilities

References

• GitHub Repository 
• SciPy Statistical Functions: https://docs.scipy.org/doc/scipy/reference/stats.html 
• FastAPI Framework: https://fastapi.tiangolo.com/ 
• ANOVA Methodology: https://en.wikipedia.org/wiki/Analysis_of_variance 
• Multiple Comparison Corrections: https://en.wikipedia.org/wiki/Bonferroni_correction 
• Effect Size (Cohen’s d): https://en.wikipedia.org/wiki/Effect_size