Machine learning (ML) can be resource intensive. To maximize your ML investments, high-performance and cost-effective solutions are needed so you can use ML models in production at scale. In this session, we present use cases for deploying machine learning models using Amazon SageMaker. We discuss optimized infrastructure choices; real-time, autoscaling asynchronous, serverless, and batch endpoint deployment options; multi-container endpoints; multi-model endpoints; model monitoring; and CI/CD integration for your ML workloads.
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