Edge AI: From Concept to Implementation

• 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

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 intermediate-level developers and IT professionals who wish to gain a comprehensive understanding of Edge AI from concept to practical implementation, including setup and deployment.

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

• Understand the fundamental concepts of Edge AI.
• Set up and configure Edge AI environments.
• Develop, train, and optimize Edge AI models.
• Deploy and manage Edge AI applications.
• Integrate Edge AI with existing systems and workflows.
• Address ethical considerations and best practices in Edge AI implementation.

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 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