DataPalooza - ML + IoT Workshop: San Francisco Loft

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DataPalooza - ML + IoT Workshop at the San Francisco Loft 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 and miss out on having fun. You are looking to use Machine Learning and 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, and IoT and 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 and emotion detection, but there are two ways that the predictions (inference) can be done; on the cloud and on the camera itself, but which one will work the best for your needs at the festival? You are going to test both approaches and find out! In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and 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, and AWS Lambda to build an end-to-end IoT solution that performs machine learning. Speakers: Daniel Whitehead - Solutions Architect, AWS Jyothi Nookula - Sr. Product Manager (Technical), DeepCam, AWS

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Pop-up Loft
DataPalooza
Daniel Whitehead, Solutions Architect
Jyothi Nookula, Senior Product Manager

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Agenda
• Team Introductions:
• Video: What is DataPalooza?
• DataPalooza Workshop Objectives:
• Machine Learning Overview
• AWS DeepLens Overview
• Hands-on lab 1: Face Detection
• Lunch Break
• Hands-on lab 2: Sentiment Analysis
• Questions / Close

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
DataPalooza—A music festival themed ML & IoT Workshop
• Scenario: Your bold startup has taken the challenge of providing a new type of EDM music festival
experience. At venues with multiple stages, festival-goers are always looking to identify which DJ stage
areas are the liveliest. This causes them to constantly move around between different stages and miss
out. You are looking to use Machine Learning and IoT to come up with a connected
fan experience that takes the music festival scene to the next level. From your initial research there are
existing ML models that you can leverage to do face and emotion detection, but there are two ways
that the predictions (inference) can be done; on the cloud and on the camera itself, but which one will
work the best for your needs at the festival?
• You are going to learn about both approaches and find out!
• In this workshop you will use AWS and Intel technologies including Amazon SageMaker with Intel C5
Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, AWS Lambda
• The objective of the workshop is to learn how to build and deploy a machine learning model and then
run inference on it from the cloud and from the edge device.
• By the time you’re done with these challenges, EDM DJ’s will be able to tell whether the crowd is
enjoying their set by the looks on their faces

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Workshop Objectives – What are today’s
objectives?
Lab Name Description AWS Services &
Categories
1 Face Detection AWS Deeplens,
Lambda
2 Sentiment Analysis AWS Deeplens,
Lambda, DynamoDB,
CloudWatch and
Rekognition
3 Create and deploy custom
model
AWS Deeplens,
Sagemaker, Lambda

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Machine Learning Overview

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
How does Machine Learning work?

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Intro to AWS & AWS DeepLens

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Labs

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Pop-up Loft
aws.amazon.com/activate
Everything and Anything Startups
Need to Get Started on AWS

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Lab 1- Face Detection
Objectives:
• Register and configure your DeepLens device
• Train a face detection model in Amazon SageMaker
URL-https://bit.ly/2pydfew

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Lab 2- Sentiment analysis
Objectives:
• Deploy face detection model
• Upload detected faces to S3
• Recognize emotions using AWS Rekognition and
Lambda
• Build sentiment analysis dashboard in CloudWatch
URL- https://bit.ly/2pydfew

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Winners of the DeepLens Hackathon
First place Second place Third place
ReadToMe
Created by Alex Schultz
ReadToMe is a deep learning
enabled application that is
able to read books to kids. In
this case, reading Green Eggs
and Ham, by Dr. Seuss.
Dee
Created by Matthew Clark
Dee is a fun AWS DeepLens
interactive device for children.
The device asks children to
answer questions by showing a
picture of the answer.
SafeHaven
Created by Nathan Stone
and Peter McLean
SafeHaven uses Alexa and
AWS DeepLens to bring
peace of mind for vulnerable
people and their families.
View all 23 projects at: https://aws.amazon.com/deeplens/community-projects

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Questions?

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved
Thank you!

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by Gitansh Chadha, Solutions Architect AWS Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.

Building Your Smart Applications with Machine Learning on AWS | AWS Webinar

Amazon Web Services

Machine Learning (ML) has long been an arcane topic, accessible only to experts. In this webinar, you will learn how to easily add Amazon API-driven ML services to your education software. Image and video analysis, text-to-speech, speech-to-text, translation, natural language processing: all these are just an API call away. Through code-level demos, we'll show you how to quickly start integrating these services into your education offerings, with zero ML expertise required. Speaker: Julien Simon, Principal Evangelist AI/ML EMEA, Amazon Web Services Learn more: https://aws.amazon.com/education View the video recording here: https://youtu.be/Dsj5KgER6ec

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DataPalooza - ML + IoT Workshop: San Francisco Loft

2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Agenda • Team Introductions: • Video: What is DataPalooza? • DataPalooza Workshop Objectives: • Machine Learning Overview • AWS DeepLens Overview • Hands-on lab 1: Face Detection • Lunch Break • Hands-on lab 2: Sentiment Analysis • Questions / Close

3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved DataPalooza—A music festival themed ML & IoT Workshop • Scenario: Your bold startup has taken the challenge of providing a new type of EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify which DJ stage areas are the liveliest. This causes them to constantly move around between different stages and miss out. You are looking to use Machine Learning and IoT to come up with a connected fan experience that takes the music festival scene to the next level. From your initial research there are existing ML models that you can leverage to do face and emotion detection, but there are two ways that the predictions (inference) can be done; on the cloud and on the camera itself, but which one will work the best for your needs at the festival? • You are going to learn about both approaches and find out! • In this workshop you will use AWS and Intel technologies including Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, AWS Lambda • The objective of the workshop is to learn how to build and deploy a machine learning model and then run inference on it from the cloud and from the edge device. • By the time you’re done with these challenges, EDM DJ’s will be able to tell whether the crowd is enjoying their set by the looks on their faces

5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Workshop Objectives – What are today’s objectives? Lab Name Description AWS Services & Categories 1 Face Detection AWS Deeplens, Lambda 2 Sentiment Analysis AWS Deeplens, Lambda, DynamoDB, CloudWatch and Rekognition 3 Create and deploy custom model AWS Deeplens, Sagemaker, Lambda

21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Lab 1- Face Detection Objectives: • Register and configure your DeepLens device • Train a face detection model in Amazon SageMaker URL-https://bit.ly/2pydfew

22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Lab 2- Sentiment analysis Objectives: • Deploy face detection model • Upload detected faces to S3 • Recognize emotions using AWS Rekognition and Lambda • Build sentiment analysis dashboard in CloudWatch URL- https://bit.ly/2pydfew

23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved Winners of the DeepLens Hackathon First place Second place Third place ReadToMe Created by Alex Schultz ReadToMe is a deep learning enabled application that is able to read books to kids. In this case, reading Green Eggs and Ham, by Dr. Seuss. Dee Created by Matthew Clark Dee is a fun AWS DeepLens interactive device for children. The device asks children to answer questions by showing a picture of the answer. SafeHaven Created by Nathan Stone and Peter McLean SafeHaven uses Alexa and AWS DeepLens to bring peace of mind for vulnerable people and their families. View all 23 projects at: https://aws.amazon.com/deeplens/community-projects

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