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Experimentation for Engineers From A/B testing to Bayesian optimization: monitoring

 
Greenhorn
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IntelliJ IDE Spring Java
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I was wondering how deep this book goes into the technical follow-up/testing of experimentation (resource consumption, event monitoring, ...), or is it more focused on retention rate/hot spots/user feedback cycles.
 
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The book describes and explains experimental methods from a mathematical and implementation (Python+NumPy) perspective, identifies common pitfalls and how to avoid them, and delves into some organization considerations. It doesn't talk about experimentation infrastructure.
 
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