Productionize ML workloads using Amazon SageMaker MLOps
Date:
This talk presented a comprehensive overview of MLOps on AWS, covering the journey from experimental notebooks to production-ready ML systems using Amazon SageMaker. Starting from the premise that ML code is only a small fraction of a real-world ML system, the session walked through an MLOps maturity framework across four phases — Initial, Repeatable, Reliable, and Scalable — mapping each to specific AWS services and capabilities. Topics included SageMaker Studio for experimentation, SageMaker Experiments for tracking, SageMaker Pipelines for workflow automation, Model Registry for versioning and promotion, SageMaker Projects for one-click CI/CD provisioning, shadow testing and deployment guardrails, Model Monitor for drift detection, and Model Cards and Dashboard for governance. The talk also covered team structures, multi-account strategies, and custom project templates for enterprise-scale MLOps.