What is dynamic model selection using contextual bandits?

Contextual bandits dynamically choose the best model based on input context and reward feedback.

Contextual bandits are reinforcement learning algorithms that balance exploration and exploitation. In MLOps, they are used for model selection, choosing between multiple models based on user context and past performance. The system continuously learns which model performs best for different segments of input space.