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Running Effective Offline Machine Learning Experiments

Machine learning experiments are vital for improving models’ accuracy, but there are some challenges to running them offline. Find out how engineers at Sift Science established a framework that allows us to run and evaluate experiments effectively.

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Read this article to discover:

  • Steps to minimize bias in offline experiments
  • How we evaluate and analyze our experiments
  • What tools we use to bake in correctness

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