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Course Outline
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
Requirements
- 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
14 Hours
