Course Outline

Introduction to Edge AI

  • Definition and key concepts
  • Differences between Edge AI and Cloud AI
  • Benefits and challenges of Edge AI
  • Overview of Edge AI applications

Edge AI Architecture

  • Components of Edge AI systems
  • Hardware and software requirements
  • Data flow in Edge AI applications
  • Integration with existing systems

Setting Up the Edge AI Environment

  • Introduction to Edge AI platforms (Raspberry Pi, NVIDIA Jetson, etc.)
  • Installing necessary software and libraries
  • Configuring the development environment
  • Initializing the Edge AI setup

Developing Edge AI Models

  • Overview of machine learning and deep learning models for edge devices
  • Training models specifically for edge deployment
  • Techniques for optimizing models for edge devices
  • Tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.)

Data Management and Preprocessing for Edge AI

  • Data collection techniques for edge environments
  • Data preprocessing and augmentation for edge devices
  • Managing data pipelines on edge devices
  • Ensuring data privacy and security in edge environments

Deploying Edge AI Applications

  • Steps for deploying models on various edge devices
  • Techniques for monitoring and managing deployed models
  • Real-time data processing and inference on edge devices
  • Case studies and practical examples of deployment

Integrating Edge AI with IoT Systems

  • Connecting Edge AI solutions with IoT devices and sensors
  • Communication protocols and data exchange methods
  • Building an end-to-end Edge AI and IoT solution
  • Practical examples and use cases

Use Cases and Applications

  • Industry-specific applications of Edge AI
  • In-depth case studies in healthcare, automotive, and smart homes
  • Success stories and lessons learned
  • Future trends and opportunities in Edge AI

Ethical Considerations and Best Practices

  • Ensuring privacy and security in Edge AI deployments
  • Addressing bias and fairness in Edge AI models
  • Compliance with regulations and standards
  • Best practices for responsible AI deployment

Hands-On Projects and Exercises

  • Developing a complex Edge AI application
  • Real-world projects and scenarios
  • Collaborative group exercises
  • Project presentations and feedback

Summary and Next Steps

Requirements

  • An understanding of basic AI and machine learning concepts
  • Experience with programming languages (Python recommended)
  • Familiarity with edge computing and IoT concepts

Audience

  • Developers
  • IT professionals
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories