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Would You Have Clicked on What We Would Have Recommended?
Peter B. Golbus describes recent work on the offline estimation of recommender system A/B tests using counterfactual reasoning techniques.
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End-to-End ML without a Data Scientist
Holden Karau discusses how to train models, and how to serve them, including basic validation techniques, A/B tests, and the importance of keeping models up-to-date.
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Pricing Page Optimization
Elena Verna discusses user behavior on the pricing page and how to organize the A/B testing resources to optimize the pricing page, one of the most important funnels of a website.
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Query Understanding: a Manifesto
Daniel Tunkelang talks about what search looks like when viewed through a query understanding mindset. He focuses on query performance prediction, query rewriting, and search suggestions.
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Machine Learning at Netflix Scale
Aish Fenton discusses Netflix' machine learning algorithms, including distributed Neural Networks on AWS GPUs, providing insight into offline experimentation and online AB testing.
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A/B Testing + Continuous Delivery = Everyday Product Launches
Nellwyn Thomas discusses how Etsy is using A/B testing, how Etsy's data-driven culture has evolved over time and how continuous delivery and big data can coexist.
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From Experiments to Insights at Pinterest
Andrea Burbank discusses the evolution of Pinterest's A/B testing platform and how one can learn from their mistakes to go from simply running experiments to actually deriving insights.
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A/B Testing: Lessons Learned at Spotify
Danielle Jabin shares some of Spotify's key takeaways from their A/B testing efforts and the challenges they faced in building out their A/B testing infrastructure.
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Managing Experimentation in a Continuously Deployed Environment
Wil Stuckey explains how Etsy manages to deploy nearly ~10,000 changes in one year, and how they run A/B experiments in the midst of continual code change.
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