In this workshop, we step through the key features of AWS Greengrass Machine Learning (ML) Inference. First, we explore how to use Amazon SageMaker to build an ML model. We then install and run it on an AWS Greengrass device. AWS Greengrass ML Inference gives you the best of both worlds. You use the cloud to build and train models and ML inference to deploy and run the model locally for quick response times and local action.

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Building an AWS Greengrass Machine
Learning Solution at the Edge
Philipp Sacha
Specialist Solutions Architect IoT
Amazon Web Services
I O T 4 0 3

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Agenda
Workshop setup
AWS Greengrass
Amazon SageMaker
Hands-on exercises

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Workshop setup
Prerequisites
AWS Account with admin privileges
Laptop, browser
Amazon Elastic Compute Cloud (Amazon EC2) provisioned through AWS
CloudFormation
EC2 instance with Opensource Cloud9 IDE to run AWS Greengrass on. Because of the time window of
2:15h for the workshop, you’ll use a virtual machine and no real hardware
Amazon SageMaker
Train an ML model for image classification based on the CIFAR-10 dataset
Download ML model to AWS Greengrass and classify images at the edge

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AWS Cloud9 IDE

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Workshop and beyond
• Upon finishing the workshop, you should have built an end-to-end example to
train a machine learning model and use it at the edge
• You could use the workshop as a base for your own solution
• Use real hardware, camera, sensors
• Select and train a model for your use case

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Use cases
Some use cases for ML at the edge
• Image classification, security camera, object detection
• Save bandwidth; make classifications even when offline
• Occupancy in rooms/buildings: Count people
• Predictive maintenance: Measure vibrations and noise level and take actions
like sending alerts or powering off machines

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AWS Greengrass – Group concept

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Lambdas
Created in AWS Lambda
Versioned (mandatory)
Aliased (optional)
Pinned or on-demand
Can retrieve credentials to access other AWS services through
Token Exchange Services (TES)

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ML inference
Train models in the cloud, for example with Amazon
SageMaker
Add models as resources to Lambdas, and deploy them to your
Greengrass core
Perform machine learning (ML) inference locally on AWS
Greengrass even if there is no network connection

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Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high-
performance
algorithms
One-click
training
Hyperparameter
optimization
Build Train Deploy

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Jupyter Notebook

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AWSIoTCore
AWS
Lambda
2.Download model
1.Trainmodelforimage
classification
4.Publishimage
classificationresult
3.Long-running Lambda
functiononAWS
Greengrass consumes
modelandclassifiesimages
AWSGreengrassAmazonSageMaker
Architecture

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http://bit.ly/greengrass-ml-workshop

18. Thank you!
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