A/B Testing ML Models In Production: Why, When, & How

A/B testing is a staple of software development and marketing, but it's often overlooked in the world of machine learning.Why bother A/B testing your ML models when you can just let them loose in the wild and see what happens?It turns out that there are several good reasons to A/B test your ML models before and while delivering them to production.Join Yuval Fernbach, Qwak’s co-founder and CTO to learn more about ML model deployment strategies:

  • How Shadow, Canary and AB deployment methodologies work for ML Models
  • Why A/B testing is important for ML
  • When you should A/B test, and how to go about setting up an effective test.

By the end of this session, you'll have an understanding of ML models deployment strategies and how to plan your model rollout effectively.

Yuval Fernbach

Yuval Fernbach

CTO & Co-founder

A/B Testing ML Models In Production: Why, When, & How

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Brand Leaders are Talking About Qwak

Oren Neiberg
Machine Learning Engineer
“With Qwak we were able to improve our ML delivery dramatically.”

Or Hiltch
VP of Engineering
“Using Qwak allowed us to focus on creating a business impact rather than spending valuable time on our infrastructure setup.”
Jonathan Yaniv
Data Science Leader
“We love Qwak because it provides a unified, end-to-end solution for managing ML-based applications in production.”