
Making the LinkedIn experimentation engine 20x faster - ksec
https://engineering.linkedin.com/blog/2020/making-the-linkedin-experimentation-engine-20x-faster
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kirillzubovsky
Interesting article on the technical aspects of scaling such a service, no
doubt. One question remains - was any of this necessary? It's amazing how much
work their machines are doing to analyze the data, and it sounds like all
decisions are made on data, but I would love to see is an article that defends
all this data in exchange of humans making decisions. Is this algorithmic
approach optimal for the outcome, or is it optimal for decision making? Would
love for someone from LIN to explain the big picture!

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volument
> It serves about 35,000 concurrently running A/B experiments,

With that amount of optimizations, you would expect them to have the best user
experience in the world. Do they? I think there is something severely wrong
with the current A/B testing methodology.

