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Duration 14 hours
Course Outline
Introduction to Edge AI and IoT
- Defining Edge AI and exploring its fundamental principles
- Surveying IoT system structures and designs
- Weighing the advantages and hurdles of combining Edge AI with IoT
- Examining practical applications and real-world scenarios
Edge AI Architecture in IoT Contexts
- Identifying the core elements of Edge AI systems for IoT
- Reviewing necessary hardware and software prerequisites
- Tracing data pathways in IoT apps powered by Edge AI
- Integrating with pre-existing IoT infrastructures
Configuring the Edge AI and IoT Workspace
- Getting acquainted with leading IoT platforms (e.g., Arduino, Raspberry Pi, NVIDIA Jetson)
- Installing essential software packages and libraries
- Adjusting the development environment for optimal performance
- Initializing the Edge AI and IoT configuration
Crafting AI Models for IoT Hardware
- Surveying machine learning and deep learning models suited for edge and IoT
- Training and refining models for IoT deployment
- Leveraging tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.)
- Applying methods for model compression and efficiency
Data Handling and Preprocessing in IoT
- Techniques for gathering data in IoT settings
- Preprocessing and enhancing data for edge device compatibility
- Orchestrating data pipelines on IoT hardware
- Safeguarding data privacy and security within IoT environments
Releasing Edge AI Models on IoT Hardware
- Process for deploying AI models onto IoT edge devices
- Strategies for overseeing and maintaining active models
- Executing real-time data processing and inference on IoT devices
- Reviewing case studies and practical deployment examples
Connecting Edge AI with IoT Protocols and Platforms
- Overview of IoT communication standards (MQTT, CoAP, HTTP, etc.)
- Linking Edge AI solutions with IoT sensors and actuators
- Constructing comprehensive Edge AI and IoT solutions
- Exploring practical examples and application cases
Applications and Use Cases
- Sector-specific implementations of Edge AI in IoT
- Detailed case studies in smart homes, industrial IoT, healthcare, and other fields
- Analyzing success stories and extracting key lessons
- Identifying future trends and opportunities in Edge AI for IoT
Ethical Implications and Best Practices
- Prioritizing privacy and security in Edge AI and IoT rollouts
- Mitigating bias and ensuring fairness in AI models
- Adhering to regulatory standards and compliance requirements
- Adopting best practices for responsible AI deployment in IoT
Practical Projects and Drills
- Building an advanced Edge AI application for IoT
- Working on real-world projects and scenarios
- Participating in collaborative group tasks
- Presenting projects and receiving constructive feedback
Recap and Future Directions
Requirements
- A solid grasp of fundamental AI and machine learning principles.
- Practical experience with programming languages, with a preference for Python.
- Basic proficiency in IoT concepts and associated technologies.
Target Audience
- IoT Developers
- System Architects
- Industry Professionals
Testimonials (1)
That we can cover advance topic and work with real-life example