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 Duration 14 hours

Course Outline

Introduction to Edge AI and IoT

  • Defining Edge AI and exploring its fundamental principles
  • Surveying IoT system structures and designs
  • Weighing the advantages and hurdles of combining Edge AI with IoT
  • Examining practical applications and real-world scenarios

Edge AI Architecture in IoT Contexts

  • Identifying the core elements of Edge AI systems for IoT
  • Reviewing necessary hardware and software prerequisites
  • Tracing data pathways in IoT apps powered by Edge AI
  • Integrating with pre-existing IoT infrastructures

Configuring the Edge AI and IoT Workspace

  • Getting acquainted with leading IoT platforms (e.g., Arduino, Raspberry Pi, NVIDIA Jetson)
  • Installing essential software packages and libraries
  • Adjusting the development environment for optimal performance
  • Initializing the Edge AI and IoT configuration

Crafting AI Models for IoT Hardware

  • Surveying machine learning and deep learning models suited for edge and IoT
  • Training and refining models for IoT deployment
  • Leveraging tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.)
  • Applying methods for model compression and efficiency

Data Handling and Preprocessing in IoT

  • Techniques for gathering data in IoT settings
  • Preprocessing and enhancing data for edge device compatibility
  • Orchestrating data pipelines on IoT hardware
  • Safeguarding data privacy and security within IoT environments

Releasing Edge AI Models on IoT Hardware

  • Process for deploying AI models onto IoT edge devices
  • Strategies for overseeing and maintaining active models
  • Executing real-time data processing and inference on IoT devices
  • Reviewing case studies and practical deployment examples

Connecting Edge AI with IoT Protocols and Platforms

  • Overview of IoT communication standards (MQTT, CoAP, HTTP, etc.)
  • Linking Edge AI solutions with IoT sensors and actuators
  • Constructing comprehensive Edge AI and IoT solutions
  • Exploring practical examples and application cases

Applications and Use Cases

  • Sector-specific implementations of Edge AI in IoT
  • Detailed case studies in smart homes, industrial IoT, healthcare, and other fields
  • Analyzing success stories and extracting key lessons
  • Identifying future trends and opportunities in Edge AI for IoT

Ethical Implications and Best Practices

  • Prioritizing privacy and security in Edge AI and IoT rollouts
  • Mitigating bias and ensuring fairness in AI models
  • Adhering to regulatory standards and compliance requirements
  • Adopting best practices for responsible AI deployment in IoT

Practical Projects and Drills

  • Building an advanced Edge AI application for IoT
  • Working on real-world projects and scenarios
  • Participating in collaborative group tasks
  • Presenting projects and receiving constructive feedback

Recap and Future Directions

Requirements

  • A solid grasp of fundamental AI and machine learning principles.
  • Practical experience with programming languages, with a preference for Python.
  • Basic proficiency in IoT concepts and associated technologies.

Target Audience

  • IoT Developers
  • System Architects
  • Industry Professionals

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