Broadcast Date: June 24, 2022

Level: 200

MLOps practices help accelerate and streamline the ML development lifecycle. ML engineers, join us for a hands-on demo showing how to use Amazon SageMaker to implement MLOps practices, including automating ML workflows, building CI/CD pipelines for ML, monitoring models in production, and standardizing model governance.

Learning Objectives

  • Explore how to automate ML workflows to accelerate data preparation and model building, training, and experiments
  • Learn how to build continuous integration and delivery (CI/CD) pipelines to reduce model management overhead
  • Find out how to monitor quality of ML models by automatically detecting bias, model drift, and concept drift

Who Should Attend?

ML engineers, MLOps Engineers, data scientists


  • Michael Hsieh - Sr. AI/ML Specialist SA
  • Shelbee Eigenbrode - Principla AI/ML Specialist SA

Learn More

To learn more about the services featured in this talk, please visit:

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Service How To

December 19th, 2018 | 1:00 PM PT

Developing Deep Learning Models for Computer Vision with
Amazon EC2 P3 Instances.

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What's New / Cloud Innovation

December 11th, 2018 | 1:00 PM PT


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Data Lakes & Analytics

Webinar 1:

What's New / Cloud Innovation

December 10th, 2018 | 11:00 AM PT


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Webinar 2:

What's New / Cloud Innovation

December 12th, 2018 | 11:00 AM PT


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