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Building Intelligent Solutions with AWS IoT


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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.

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Building Intelligent Solutions with AWS IoT

  1. 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. 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. 3. Introduction
  4. 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
  5. 5. Amazon Machine Learning Create ML models without having to learn complex algorithms and technology
  6. 6. 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
  7. 7. 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
  8. 8. Build model Validate & optimize Make predictions 1 2 3 Development process
  9. 9. Model Evaluation and Optimization
  10. 10. 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
  11. 11. 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
  12. 12. Training Parameters Model Size Regularization Data Shuffling # of Passes
  13. 13. 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
  14. 14. Labs
  15. 15. 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:
  16. 16. Thank you!