Edge AI for IoT Applications

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

Audience

• IoT developers
• System architects
• Industry professionals

Edge AI for IoT Applications focuses on the integration of Edge AI with IoT systems, enabling real-time data processing and decision-making directly on IoT devices. This course covers the foundational concepts, tools, and techniques required to implement Edge AI in IoT environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing IoT applications with intelligent data processing and analytics capabilities.

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

• Understand the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in IoT systems.
• Integrate Edge AI with various IoT protocols and platforms.
• Address ethical considerations and best practices in Edge AI for IoT.

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 and IoT

• Definition and key concepts of Edge AI
• Overview of IoT systems and architectures
• Benefits and challenges of integrating Edge AI with IoT
• Real-world applications and use cases

Edge AI Architecture for IoT

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

Setting Up the Edge AI and IoT Environment

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

Developing AI Models for IoT Devices

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

Data Management and Preprocessing in IoT

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

Deploying Edge AI Models on IoT Devices

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

Integrating Edge AI with IoT Protocols and Platforms

• Overview of IoT communication protocols (MQTT, CoAP, HTTP, etc.)
• Connecting Edge AI solutions with IoT sensors and actuators
• Building end-to-end Edge AI and IoT solutions
• Practical examples and use cases

Use Cases and Applications

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

Ethical Considerations and Best Practices

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

Hands-On Projects and Exercises

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

Summary and Next Steps