Broadcast Date: March 18, 2019
Machine learning (ML) workflows can be orchestrated with Amazon SageMaker and AWS Step Functions. With SageMaker, you can build, train, and deploy ML models quickly and easily at scale. With Step Functions, you can add resilient serverless workflows to your applications. Workflows on Step Functions require less code to write and maintain. In this tech talk, we will talk about combining the best of both worlds by using Step Functions to automate and orchestrate ML workflows with Amazon SageMaker for an end-to-end experience for developers. AWS Step Functions will monitor the SageMaker jobs, enabling a seamless experience for your workflows.
- Learn about how ML workflows can be orchestrated with the rich features of Amazon SageMaker and AWS Step Functions
- Learn about adding resilient workflows to your software applications
- Learn to automate ML workflows with less code to write and maintain
Who Should Attend?
Machine Learning Practitioners, Developers, Data Scientists, Technical Decision Makers, Architects
- Julien Simon, Global Evangelist, AWS
To learn more about the services featured in this talk, please visit:
Service How To
December 19th, 2018 | 1:00 PM PT
Developing Deep Learning Models for Computer Vision with
Amazon EC2 P3 Instances.