Machine Learning Times
Machine Learning Times
Elon Musk Predicts Artificial General Intelligence In 2 Years. Here’s Why That’s Hype
 Originally published in Forbes, April 10, 2024 When OpenAI’s...
Survey: Machine Learning Projects Still Routinely Fail to Deploy
 Originally published in KDnuggets. Eric Siegel highlights the chronic...
Three Best Practices for Unilever’s Global Analytics Initiatives
    This article from Morgan Vawter, Global Vice...
Getting Machine Learning Projects from Idea to Execution
 Originally published in Harvard Business Review Machine learning might...

4 years ago
Multi-Armed Bandits and the Stitch Fix Experimentation Platform


Multi-armed bandits have become a popular alternative to traditional A/B testing for online experimentation at Stitch Fix. We’ve recently decided to extend our experimentation platform to include multi-armed bandits as a first-class feature. This post gives an overview of our experimentation platform architecture, explains some of the theory behind multi-armed bandits, and finally shows how we incorporate them into our platform.

Primer: The Stitch Fix Experimentation Platform

Before getting into the details of multi-armed bandits, you’ll first need to know a little bit about how our experimentation platform works. In our previous post on building a centralized experimentation platform, we explained the #oneway philosophy, and how it makes experimentation both less costly and more impactful. The idea is to have #oneway to run and analyze experiments across the entire business. The same platform is used by front-end engineers, back-end engineers, product managers, and data scientists. And it’s flexible enough to be used for experiments on inventory management and forecasting, warehouse operations, outfit recommendations, marketing, and everything in between. To enable such a wide variety of experiments, we rely on two key concepts: configuration parameters and randomization units.

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