Abstract: In the era of artificial intelligence, A/B Test experiment is the core dependence of data-driven organizations. A good experimental system can support rapid product iteration, speak with data, and assist in making correct product decisions quickly. So how do you keep teams of hundreds or thousands of people iterating efficiently in super complex systems? How to produce report quickly under large scale data volume? How to reduce sampling volatility as quickly as possible to produce reliable data conclusions? This paper mainly discusses the specific practice and thinking of A/B Test platform in the actual production environment from the above aspects.
Reading time: 9 minutes.
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