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September 27, 2020 110 mins

We wrap up the second way from The DevOps Handbook, while Joe has a mystery episode, Michael doesn’t like ketchup, and Allen has a Costco problem.

These show notes, in all of their full sized digital glory, can be found at https://www.codingblocks.net/episode142, where you can join the conversation, for those using their podcast player to read this.

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Treating Features as Experiments

Integrate Hypothesis Driven Development and A/B Testing

“The most inefficient way to test a business model or product idea is to build the complete product to see whether the predicted demand actually exists.”

Jez Humble
  • Constantly ask should we build it and why? A/B testing will allow us to know if an idea is worthwhile because allows for fast-feedback on what’s working.
  • Doing these experiments during peak season can allow you to out-experiment the competition.
    • But this is only possible if you can deploy quickly and reliably.
      • This allows A/B testing to support rapid, high-velocity experiments.
  • A/B test is also known as “split testing”.
  • A/B testing is where one group sees one version of a page or feature and the other group sees another version.
  • Study from Microsoft found that only about 1/3 of features actually improved the key metric they were trying to move!
    • The important takeaway? Without measuring the impact of features, you don’t know if you’re adding value or decreasing it while increasing complexity.

Integrate A/B Testing Into Releases

  • Effective A/B testing is only possible with the ability to do production releases quickly and easily.
  • Using feature flags allow you to delivery multiple versions of the application without requiring separate hardware to be deployed to.
    • This requires meaningful telemetry at every level of the stack to understand how the application is being used.
  • Etsy open-
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