Safely Deploying ML Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing)
Deploying a new machine learning model to production is one of the most important phases of the ML lifecycle. Even if the model works well in validating and testing data sets, directly replacing an existing production model can be risky. Offline experiments rarely capture the full complexity of a real-world environment—data distributions can change, user … Read more