Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sagemaker


ON-DEMAND


Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sagemaker



Broadcast Date:
April 26, 2018

Level 200 | Service Intro
IoT edge applications are rapidly becoming more intelligent. They can not only collect data, but also perform predictive analytics and respond to local events, even with intermittent cloud connectivity. Businesses that are adopting this technology are able to gain value by developing and adopting smarter devices to become more efficient. AWS Greengrass lets you run local compute, messaging, data caching, sync, and machine learning (ML) inference capabilities for your connected devices, while Amazon SageMaker provides a complete machine learning platform to build, train, and deploy for your ML models. In this tech talk, you’ll learn how AWS Greengrass and Amazon SageMaker enable you to perform machine learning at the edge. We will focus on various business use cases and a demo of our technologies.

Learning Objectives:
• Develop intelligent IoT edge solutions using AWS Greengrass
• Develop data science models in the cloud with Amazon SageMaker
• Learn how AWS Greengrass and Amazon SageMaker enable you to perform machine learning at the edge

Suited For: Data Scientists, Developers, IoT Device Engineers, Cloud Architects, Business Decision Makers

Speaker(s): Jason Chen, Principal Product Manager, AWS; Mahendra Bairagi, Sr. IoT Specialist SA, AWS


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