Shadow deployments are a strategy used in machine learning and software development to test new model versions or application updates without impacting the user experience. In this approach, a new model, referred to as the shadow variant, runs alongside the existing production model, capturing a portion of the incoming requests. This allows developers to evaluate the performance of the new model in real-time while the production model continues to serve users. By analyzing the results from the shadow variant, teams can ensure that any potential issues are identified before fully transitioning to the new model, thereby minimizing risks and downtime.