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Implement MLOps practices with Amazon SageMaker

Implementing the right MLOps practices enables builders to collaborate effectively in preparing, building, training, deploying, and managing models at scale. In this session, we will showcase the MLOps features in Amazon SageMaker that assist in provisioning consistent model development environments, automating ML workflows, implementing CI/CD pipelines for ML, monitoring models in production, and standardizing model governance capabilities. We will demonstrate how to apply these MLOps practices using Amazon SageMaker features — such as SageMaker projects, SageMaker Pipelines, SageMaker Model Registry, and SageMaker Model Monitor — to quickly deliver high-performance production ML models at scale.

Gaurav Singh, Senior Solutions Architect, AWS India
Smiti Guru, Senior Solutions Architect, AWS India