Shadow deployments are a modern strategy used in machine learning and software development to test new model versions without impacting the user experience. This approach allows developers to run multiple versions of a model in parallel, where the new version, known as the shadow variant, processes a portion of the incoming requests while the production variant continues to serve the majority. By capturing and analyzing the performance of the shadow variant, teams can validate its effectiveness and make informed decisions about promoting it to production. This minimizes risks associated with deploying potentially low-performing models and ensures a smoother transition.