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
  • Training models for edge deployment
  • Model optimization techniques
  • Tools and frameworks for Edge AI development

Deploying Edge AI Applications

  • Steps for deploying models on edge devices
  • Monitoring and managing deployed models
  • Real-time data processing and inference
  • Case studies and examples

Use Cases and Applications

  • Industry-specific applications of Edge AI
  • 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
  • Addressing bias and fairness
  • Compliance with regulations and standards
  • Best practices for responsible AI deployment

Hands-On Projects and Exercises

  • Developing a simple 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 general computing concepts

Audience

  • Developers
  • IT professionals
 14 Hours

Delivery Options

Private Group Training

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  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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