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With Daniel Whitehead (Soln Architect, AWS) & Jyothi Nookula (Technical Production Mgr DeepCam, AWS).
Wednesday, May 22, 2019 at 10:00 AM   Absolutely Free
Amazon AWS Loft, 350 W Broadway

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As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages & miss out on having fun. You are looking to use Machine Learning & IoT technologies to solve this unique problem.

Do you accept the Challenge?
The objective of this task is to help the festival-goers quickly identify the DJ stage where crowd is the happiest. You've seen a lot of buzz around computer vision, machine learning, & IoT & want to use this technology to detect crowd emotions. From your initial research there are existing ML models that you can leverage to do face & emotion detection, but there are two ways that the predictions (inference) can be done; on the cloud & on the camera itself, but which one will work the best for your needs at the festival? You are going to test both approaches & find out!

Who should attend

In this workshop you will use AWS & Intel technologies to learn how to build, deploy, & run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, & AWS Lambda to build an end-to-end IoT solution that performs machine learning.

This event is for Machine Learning & IoT developers. In order to attend this workshop, you must have an active AWS account ( with administrative rights, either for personal or business use.

Level: Intermediate to Advanced.


MAY 22




Introduction & Overview of AWS & Intel Technologies

Overview of AWS & Intel technologies related to Machine Learning & IoT. We'll cover an overview of Sagemaker, DeepLens, Greengrass, Intel C5 instances. We'll then provide an overview of how to & how to a build a data science model.

Level: 100

Speaker: Daniel Whitehead - Solution Architect, AWS


Workshop Challenge 1 | Machine Learning on the Cloud

In this challenge, you will configure a Greengrass-enabled IoT device to act as a smart camera, which will send images to the endpoint for face-detection. You'll build out the rest of the pipeline necessary to create a dashboard for tracking crowd emotions in real-time, including AWS Lambda, Amazon DynamoDB, Amazon Rekognition, & Amazon CloudWatch.

Level: 200

Speaker: Daniel Whitehead - Solution Architect, AWS



Lunch provided courtesy of AWS.


Workshop Challenge 2 | Machine Learning at the Edge

In this challenge, you will swap out the ML/IoT part of the previous pipeline with a new pipeline that uses AWS DeepLens to run inference on the edge. DeepLens will then put face crops to the S3 bucket correctly, continuing the rest of the application pipeline.

Level: 200

Speaker: Jyothi Nookula - Sr. Production Manager (Technical), DeepCam, AWS
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