In Chapter 12, Performing a Practical Industrial IoT Solution with Azure, we learned about the functions of Azure. This technology can be easily deployed on the edge. We will see an example of this in Chapter 15, Deploying Analytics on an IoT Platform.
Azure functions on the edge
by Antonio Capasso, Giacomo Veneri
Hands-On Industrial Internet of Things
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Introduction to Industrial IoT
- Understanding the Industrial Process and Devices
- Industrial Data Flow and Devices
- Implementing the Industrial IoT Data Flow
- Discovering OPC
- Understanding the I-IoT edge
- Implementing the I-IoT data flow
- Summary
- Questions
- Further reading
- Applying Cybersecurity
- Performing an Exercise Based on Industrial Protocols and Standards
- Developing Industrial IoT and Architecture
- Implementing a Custom Industrial IoT Platform
- Understanding Industrial OEM Platforms
- Technical requirements
- I-IoT OEM platforms
- The Predix Platform
- The MindSphere platform
- Other platforms
- Summary
- Questions
- Further reading
- Implementing a Cloud Industrial IoT Solution with AWS
- Implementing a Cloud Industrial IoT Solution with Google Cloud
- Performing a Practical Industrial IoT Solution with Azure
- Understanding Diagnostics, Maintenance, and Predictive Analytics
- Technical requirements
- I-IoT analytics
- The different classes of analytics
- I-IoT analytics technologies
- Building I-IoT analytics
- Understanding the role of the infrastructure
- Deploying analytics
- Open System Architecture (OSA)
- Analytics in practice
- Summary
- Questions
- Further reading
- Implementing a Digital Twin – Advanced Analytics
- Technical requirements
- Advanced analytics and digital twins
- Advanced analytics in practice
- Platforms for digital twins
- Other kinds of I-IoT data
- Summary
- Questions
- Further reading
- Deploying Analytics on an IoT Platform
- Technical requirements
- Working with the Azure ML service
- Implementing analytics on AWS SageMaker
- Evaluating the remaining useful life (RUL) of an engine with SageMaker
- Downloading a dataset on S3
- Starting the notebook
- Working with the dataset
- Understanding the implementation of a SageMaker container
- Building the container
- Training the model locally
- Testing the model locally
- Publishing the image on AWS cloud
- Training the model in AWS SageMaker
- Testing the model on AWS SageMaker notebook
- Understanding the advanced features of SageMaker
- Consuming the model from AWS IoT Core
- Evaluating the remaining useful life (RUL) of an engine with SageMaker
- Understanding the advanced analytics capabilities of GCP 
- Discovering multi-cloud solutions
- Summary
- Questions
- Further reading
- Assessment
- Chapter 1: Introduction to Industrial IoT
- Chapter 2: Understanding the Industrial Process and Devices
- Chapter 3: Industrial Data Flow and Devices
- Chapter 4: Implementing the Industrial IoT Data Flow
- Chapter 5: Applying Cybersecurity
- Chapter 6: Performing an Exercise Based on Industrial Protocols and Standards
- Chapter 7: Developing Industrial IoT and Architecture
- Chapter 8: Implementing a Custom Industrial IoT Platform
- Chapter 9: Understanding Industrial OEM Platforms
- Chapter 10: Implementing a Cloud Industrial IoT Solution with AWS
- Chapter 11: Implementing a Cloud Industrial IoT Solution with Google Cloud
- Chapter 12: Performing a Practical Industrial IoT Solution with Azure
- Chapter 13: Understanding Diagnostics, Maintenance, and Predictive Analytics
- Chapter 14: Implementing a Digital Twin - Advanced Analytics
- Chapter 15: Deploying Analytics on an IoT Platform
- Other Books You May Enjoy
Azure functions on the edge
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