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Course Outline
Introduction to Edge AI
- Core definitions and fundamental concepts
- Distinguishing between Edge AI and cloud-based AI
- Advantages and typical use cases for Edge AI
- Survey of common edge devices and platforms
Setting Up the Edge Environment
- Introduction to hardware options (such as Raspberry Pi, NVIDIA Jetson, and others)
- Installation of essential software and libraries
- Configuration of the development workspace
- Hardware preparation for AI model deployment
Developing AI Models for the Edge
- Overview of machine learning and deep learning architectures suited for edge devices
- Methodologies for training models in both local and cloud settings
- Optimization techniques for edge deployment (including quantization and pruning)
- Essential tools and frameworks for Edge AI development (e.g., TensorFlow Lite, OpenVINO)
Deploying AI Models on Edge Devices
- Procedures for deploying AI models across various edge hardware platforms
- Real-time data processing and inference capabilities on edge devices
- Methods for monitoring and managing deployed models
- Real-world examples and case studies
Practical AI Solutions and Projects
- Creating AI applications tailored for edge devices (such as computer vision and natural language processing)
- Hands-on project: Constructing a smart camera system
- Hands-on project: Deploying voice recognition on edge devices
- Collaborative group projects addressing real-world scenarios
Performance Evaluation and Optimization
- Techniques for assessing model performance on edge hardware
- Tools for monitoring and debugging Edge AI applications
- Strategies to enhance AI model performance
- Mitigating challenges related to latency and power consumption
Integration with IoT Systems
- Connecting Edge AI solutions with IoT devices and sensors
- Review of communication protocols and data exchange mechanisms
- Building end-to-end solutions combining Edge AI and IoT
- Practical examples of system integration
Ethical and Security Considerations
- Safeguarding data privacy and security in Edge AI applications
- Mitigating bias and ensuring fairness in AI models
- Ensuring compliance with relevant regulations and standards
- Best practices for responsible AI deployment
Hands-On Projects and Exercises
- Development of a comprehensive Edge AI application
- Execution of real-world projects and scenarios
- Collaborative group exercises
- Project presentations and feedback sessions
Requirements
- A solid grasp of AI and machine learning concepts
- Proficiency in programming languages (Python is recommended)
- Basic familiarity with edge computing principles
Audience
- Developers
- Data scientists
- Technology enthusiasts
14 Hours
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
