Shadow deployment is a technique for testing new machine learning models in a live production environment without impacting end-users. This method involves running a new model alongside an existing one, directing live traffic to the established model while the new model processes the same data in parallel. The outputs of both models are then compared to evaluate the performance and accuracy of the new model before it is fully integrated. AI
IMPACT Enables safer integration of new ML models into production systems by allowing real-world testing without user disruption.
RANK_REASON The article describes a specific MLOps technique for testing ML models, which falls under tooling for AI development and deployment.
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