4 Ways to Test ML Models in Production

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 subset of users. Interleaved testing mixes predictions from both models, and shadow testing logs outputs without affecting user experience. These techniques help mitigate risks and validate the model effectively.