Machine learning (ML) has proven to be one of the most successful and widespread applications of technology, affecting a wide range of industries and impacting billions of users every day. With this rapid adoption of ML into every industry, companies are facing challenges in supporting low-latency predictions and with high availability while maximizing resource utilization and reducing associated costs. Because each ML framework has its own dependencies, and deployment steps for each framework are different, deploying models built in different frameworks in production and managing each of the endpoints becomes more and more complex.
Amazon SageMaker multi-container endpoints (MCEs) enables us to group models on different frameworks and deploy them to the same host, creating a single endpoint. You can provide containers for the different frameworks that you’re using to build the models, and SageMaker takes all of these containers and puts them behind one endpoint. For instance, you could

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