Scalable Continuous Monitoring for Large-scale A/B Experimentation
At Uber, our A/B Testing Framework and Continuous Experiment Monitoring talk reveals how we've revolutionized experimental analytics at scale. We'll demonstrate our solution to the "peeking problem" that plagues traditional experiment monitoring approaches. This presentation showcases our automated platform that processes thousands of monitoring analyses daily using regression-adjusted estimators with anytime-valid inference. This advanced statistical methodology eliminates 95% of noise without sacrificing true signals, enabling Early Experiment Detection and Performance Insights. Learn how our Spark-powered computational framework efficiently batches experiments and metrics for scalable processing. We'll share Real-World Case Studies showing how this system has transformed Uber's Data-Driven Decision Making, minimizing undetected regressions and accelerating product innovation across our global platform.