Shelbee Eigenbrode

Amazon SageMaker AI provides purpose-built capabilities that create a reliable path to production for customized AI models that power your agentic AI applications.

Amazon SageMaker provides purpose-built tools to create a reliable path to production for both machine learning and generative AI workflows. SageMaker MLOps helps you automate and standardize processes across generative AI and ML lifecycles.

Machine learning operations (MLOps) tools help you automate and standardize processes across the ML lifecycle to productionize ML models more quickly and maintain model quality in production. Amazon SageMaker provides a breadth of MLOps tools to train, test, troubleshoot, deploy, and govern ML models at scale.

In this chalk talk, explore how to build, automate, manage, and scale machine learning (ML) workflows using the AWS-native DevOps tools and services. Learn how to provision the underlying resources needed to enable CI/CD capabilities for your ML development lifecycle.

Implementing MLOps practices helps data scientists and operations engineers collaborate to prepare, build, train, deploy, and manage models at scale. During this session, explore the breadth of MLOps features in Amazon SageMaker that help you provision consistent model development environments, automate ML workflows, implement CI/CD pipelines for ML, monitor models in production, and...

MLOps practices help data scientists and IT operations professionals collaborate and manage the production machine learning (ML) workflow, including data preparation and building, training, deploying, and monitoring models. During this session, explore the breadth of features in Amazon SageMaker that help you increase automation and improve the quality of your end-to-end workflows.

In this workshop, learn how to use Amazon SageMaker Neo deep learning compiler to compile your trained TensorFlow models and deploy them on the cloud or on edge devices using AWS IoT Greengrass. Learn how Neo deep learning compiler optimizes the trained models by improving efficiency and reducing the memory footprint of the compiled model and how Neo runtime abstracts the underlying hardware...