Testing ML models in production is crucial to ensure reliability and performance on real-world data. Four common strategies are A/B testing, canary testing, interleaved testing, and shadow testing. A/B testing distributes requests non-uniformly between models, while canary testing gradually rolls out the candidate model to a

3m read timeFrom blog.dailydoseofds.com
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#1) A/B testing#2) Canary testing#3) Interleaved testing#4) Shadow testingAre you overwhelmed with the amount of information in ML/DS?

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