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Course Outline
Introduction to Edge AI
- Definition and core concepts
- Distinguishing features between Edge AI and Cloud AI
- Advantages and challenges associated with Edge AI
- Survey of Edge AI applications
Edge AI Architecture
- Key components of Edge AI systems
- Hardware and software prerequisites
- Data movement in Edge AI applications
- Integration with existing infrastructure
Establishing the Edge AI Environment
- Overview of Edge AI platforms (e.g., Raspberry Pi, NVIDIA Jetson)
- Installation of required software and libraries
- Configuration of the development workspace
- Initialization of the Edge AI setup
Developing Edge AI Models
- Overview of machine learning and deep learning models suited for edge devices
- Training models specifically for edge deployment
- Optimization techniques for edge device performance
- Tools and frameworks for Edge AI development (e.g., TensorFlow Lite, OpenVINO)
Data Management and Preprocessing for Edge AI
- Data collection methods for edge environments
- Preprocessing and augmentation for edge devices
- Management of data pipelines on edge devices
- Maintaining data privacy and security in edge environments
Deploying Edge AI Applications
- Procedures for deploying models across various edge devices
- Strategies for monitoring and managing deployed models
- Real-time data processing and inference on edge devices
- Case studies and practical deployment examples
Integrating Edge AI with IoT Systems
- Linking Edge AI solutions with IoT devices and sensors
- Communication protocols and data exchange mechanisms
- Constructing an end-to-end Edge AI and IoT solution
- Practical examples and use cases
Use Cases and Applications
- Industry-specific applications of Edge AI
- Detailed case studies in healthcare, automotive, and smart home sectors
- Success stories and lessons learned
- Emerging trends and opportunities in Edge AI
Ethical Considerations and Best Practices
- Ensuring privacy and security in Edge AI deployments
- Tackling bias and fairness in Edge AI models
- Adhering to 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
Requirements
- A foundational knowledge of AI and machine learning principles
- Proficiency in programming languages (Python is recommended)
- Familiarity with edge computing and IoT concepts
Target Audience
- Software Developers
- IT Professionals
14 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
