Minimize the production impact of ML model updates with Amazon SageMaker shadow testing

Amazon SageMaker now allows you to compare the performance of a new version of a model serving stack with the currently deployed version prior to a full production rollout using a deployment safety practice known as shadow testing. Shadow testing can help you identify potential configuration errors and performance issues before they impact end-users. With SageMaker, you don't need to invest in building your shadow testing infrastructure, allowing you to focus on model development. SageMaker takes care of deploying the new version alongside the current version serving production requests, routing a portion of requests to the shadow version. You can then compare the performance of the two versions using metrics such as latency and error rate.