In this hands-on workshop, the idea is that we provide attendees with sample code to simulate telemetry from devices (Wind Turbines), sample training data to train the machine learning models. AWS IoT rules engine will use machine learning for predicting failures and control the device when failure is predicted.

1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Neeraj Kumar
26 September 2017
Building Predictive Applications with AWS IoT &
Amazon Machine Learning
Workshop

2. Agenda
15 mins
10 mins
20mins
20 mins
20 mins
Workshop Setup
Lab 1 – Setup Machine Learning Model
Lab 3 – Create Lambda Function
Lab 2 – Setup IoT ‘Thing’ and Rule Engine
Lab 4 - Setup and Activate device code
(simulated wind turbine)
Introduction to AWS IoT & Amazon Machine Learning
20mins

3. Introduction

4. AWS IoT
Fully managed cloud platform that lets connected devices easily and securely
interact with cloud applications and other devices
Devices Network Security Data Collection Smarts
1
Connect Billions of
Devices
Lightweight Communication
Protocol
X509 Certificates DynamoDB, Kinesis,
and S3
Trigger Lambda
Functions

6. Amazon Machine Learning
Create ML models without having to learn
complex algorithms and technology

7. Amazon Machine learning
Easy to use, managed machine learning
service built for developers
Robust, powerful machine learning
technology based on Amazon’s internal
systems
Create models using your data already
stored in the AWS cloud
Deploy models to production in seconds

8. Easy to use and developer-friendly
Use the intuitive, powerful service console to build
and explore your initial models
• Data retrieval
• Model training, quality evaluation, fine-tuning
• Deployment and management
Automate model lifecycle with fully featured APIs and
SDKs
• Java, Python, .NET, JavaScript, Ruby, Javascript
Easily create smart iOS and Android applications with
AWS Mobile SDK

9. Build model Validate &
optimize
Make
predictions
1 2 3
Development process

10. Model Evaluation and Optimization

11. Batch predictions
Asynchronous predictions with trained model
Real time predictions
Synchronous, low latency, high throughput
Mount API end-point with a single click
Making predictions

12. Three supported types of predictions
Binary classification
Predict the answer to a Yes/No question
Multiclass classification
Predict the correct category from a list
Regression
Predict the value of a numeric variable

13. Training Parameters
Model Size
Regularization
Data Shuffling
# of Passes

14. High Level Design- This is what we are going to build today!
AWS Lambda
Device Gateway
Amazon Machine
Learning
wind-turbine/telemetry
IoT
rule
Device Shadow
AWS IoT
Wind Farm

15. Labs

16. Lab 1 – Setup Machine Learning Model
Lab 2 – Setup IoT ‘Thing’ and Rule Engine
Lab 3 – Create Lambda Function
Lab 4 - Setup and Activate device code (simulated wind farm)
We will go over these labs:

17. Thank you!