ML algorithms try to build a model from data. ML algorithms can be supervised, unsupervised, or reinforcement-based.
- 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
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