Broadcast Date: February 27, 2020

Level: 200

Traditional machine learning (ML) development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. Amazon SageMaker Studio solves this challenge by providing all of the components used for machine learning in a single, web-based visual interface. SageMaker Studio gives you complete access, control, and visibility into each step required to build, train, and deploy models. You can quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place, making you much more productive. In this tech talk, we will explain how it works, including a demo.

Learning Objectives

  • Understand how Amazon SageMaker Studio helps complete all steps of the ML workflow
  • Learn how Amazon SageMaker Studio helps you improve model quality
  • Understand how you can improve model performance with Amazon SageMaker Studio

Who Should Attend?

Data Scientists, Developers, Machine Learning Researchers, Machine Learning Developers


  • Julien Simon, Principal Advocate, ML/AI, AWS

Learn More

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

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