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Abstract
Real-time inference in a modern machine learning workflow requires robust deployment and monitoring to ensure models are delivering accurate and timely predictions. This paper elaborates on the implementation details of a CI/CD framework for deploying SageMaker real-time inference models by automating model packaging, deployment, and monitoring processes, integrating key approval steps that assure model performance and stakeholder involvement before production deployment. The workflow is designed to take full advantage of AWS Step Functions, SageMaker Model Registry, and other AWS services to make this transition from development to production as seamless as possible.
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