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Course Outline
Introduction to Edge AI and IoT
- Defining Edge AI and exploring its key concepts
- An overview of IoT system architectures
- Analyzing the benefits and challenges of merging Edge AI with IoT
- Examining real-world applications and use cases
Edge AI Architecture for IoT
- Identifying the core components of Edge AI systems in IoT
- Defining hardware and software prerequisites
- Mapping data flows in Edge AI-enabled IoT applications
- Strategies for integrating with existing IoT infrastructure
Establishing the Edge AI and IoT Environment
- Introduction to leading IoT platforms such as Arduino, Raspberry Pi, and NVIDIA Jetson
- Installing the required software stacks and libraries
- Configuring the development environment for optimal performance
- Initializing the setup for Edge AI and IoT integration
Building AI Models for IoT Devices
- Surveying machine learning and deep learning models suitable for edge and IoT
- Training and optimizing models specifically for IoT deployment
- Utilizing key tools and frameworks for Edge AI development (e.g., TensorFlow Lite, OpenVINO)
- Applying techniques for model compression and optimization
Data Management and Preprocessing in IoT
- Effective data collection strategies for IoT environments
- Preprocessing and augmenting data for edge device constraints
- Managing data pipelines efficiently on IoT devices
- Safeguarding data privacy and security within IoT ecosystems
Deploying Edge AI Models on IoT Devices
- A step-by-step guide to deploying AI models on IoT edge devices
- Methods for monitoring and maintaining deployed models
- Achieving real-time data processing and inference on IoT devices
- Reviewing case studies and practical deployment examples
Integrating Edge AI with IoT Protocols and Platforms
- Understanding major IoT communication protocols (MQTT, CoAP, HTTP, etc.)
- Connecting Edge AI solutions with IoT sensors and actuators
- Constructing end-to-end Edge AI and IoT solutions
- Exploring practical examples and specific use cases
Use Cases and Applications
- Industry-specific implementations of Edge AI in IoT
- In-depth case studies covering smart homes, industrial IoT, healthcare, and more
- Sharing success stories and key lessons learned
- Projecting future trends and opportunities in Edge AI for IoT
Ethical Considerations and Best Practices
- Ensuring robust privacy and security in Edge AI and IoT deployments
- Mitigating bias and promoting fairness in AI models
- Adhering to relevant regulations and industry standards
- Best practices for responsible AI deployment in IoT
Hands-On Projects and Exercises
- Building a complex Edge AI application for IoT
- Working through real-world projects and scenarios
- Participating in collaborative group exercises
- Presenting projects and receiving feedback
Summary and Next Steps
Requirements
- A solid grasp of fundamental AI and machine learning principles
- Proficiency in programming languages (Python is recommended)
- General knowledge of IoT concepts and technologies
Target Audience
- IoT developers
- System architects
- Industry professionals
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
