Introduction to Edge AI

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

Audience

• Developers
• IT professionals

Edge AI is the deployment and operation of AI models directly on edge devices, such as smartphones, IoT devices, and sensors, enabling real-time data processing and decision-making.

This instructor-led, live training (online or onsite) is aimed at beginner-level developers and IT professionals who wish to understand the fundamentals of Edge AI and its introductory applications.

By the end of this training, participants will be able to:

• Understand the basic concepts and architecture of Edge AI.
• Set up and configure Edge AI environments.
• Develop and deploy simple Edge AI applications.
• Identify and understand the use cases and benefits of Edge AI.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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