ML Product Experiments at Scale

Case Study

Today, nearly all data experimentation at Yelp—from products to AI and machine learning—occurs on the custom-built Bunsen platform, with over 700 experiments in total being run at any one time. Bunsen supports the deployment of experiments to large but segmented parts of Yelp’s customer population, and it enables the company’s data scientists to roll back these experiments if need be.

However, adapting a digital product A/B testing system to support complex ML-powered use cases required advanced techniques, highly cross-functional product, engineering and ML teamwork and a unique design approach. This talk will explore lessons learned and best practices for building robust experimentation workflows into production machine learning deployments.

Session Speakers

Vice President Data Science & Analytics

Oops, please Login or Create Account to view On Demand

The good news is that it's both easy and free to register and get access.

Account Login

Create Account

Newsletter Consent(Required)
Terms and Privacy Consent
This field is for validation purposes and should be left unchanged.