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Duration 14 hours
Course Outline
Introduction to Edge AI
- Definition and core concepts
- Distinctions between Edge AI and cloud AI
- Benefits and applicable use cases of Edge AI
- Overview of common edge devices and platforms
Setting Up the Edge Environment
- Introduction to edge devices (such as Raspberry Pi, NVIDIA Jetson, etc.)
- Installation of required software and libraries
- Configuration of the development environment
- Preparation of hardware for AI deployment
Developing AI Models for the Edge
- Overview of machine learning and deep learning models suited for edge devices
- Techniques for training models in both local and cloud environments
- Model optimization strategies for edge deployment (including quantization, pruning, etc.)
- Key tools and frameworks for Edge AI development (e.g., TensorFlow Lite, OpenVINO, etc.)
Deploying AI Models on Edge Devices
- Procedures for deploying AI models across various edge hardware
- Handling real-time data processing and inference on edge devices
- Monitoring and managing deployed models
- Practical examples and case studies
Practical AI Solutions and Projects
- Creating AI applications for edge devices (e.g., computer vision, natural language processing)
- Hands-on project: Constructing a smart camera system
- Hands-on project: Implementing voice recognition on edge devices
- Collaborative group projects and real-world scenario simulations
Performance Evaluation and Optimization
- Methods for assessing model performance on edge devices
- Tools for monitoring and debugging edge AI applications
- Strategies for optimizing AI model performance
- Mitigating latency and power consumption challenges
Integration with IoT Systems
- Connecting edge AI solutions with IoT devices and sensors
- Communication protocols and data exchange mechanisms
- Building a comprehensive end-to-end Edge AI and IoT solution
- Practical integration examples
Ethical and Security Considerations
- Safeguarding data privacy and security in Edge AI applications
- Mitigating bias and ensuring fairness in AI models
- Adhering to relevant regulations and standards
- Best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application
- Engaging with real-world projects and scenarios
- Collaborative group exercises
- Project presentations and constructive feedback
Requirements
- A solid understanding of AI and machine learning concepts
- Proficiency in programming languages (Python is recommended)
- Acknowledgment of edge computing fundamentals
Target Audience
- Developers
- Data scientists
- Tech enthusiasts
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
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