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Course Outline
Introduction to Edge AI
- Definitions and core concepts
- Distinguishing between Edge AI and cloud AI
- Key benefits and application scenarios for Edge AI
- Overview of prevalent edge devices and platforms
Configuring the Edge Environment
- Introduction to edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installing essential software and libraries
- Setting up the development environment
- Preparing hardware for AI workloads
Developing AI Models for the Edge
- Overview of machine learning and deep learning architectures suited for edge devices
- Techniques for training models in local and cloud settings
- Optimisation strategies for edge deployment (quantisation, pruning, etc.)
- Tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.)
Deploying AI Models on Edge Devices
- Processes for deploying AI models across various edge hardware
- Real-time data processing and inference on edge devices
- Monitoring and managing deployed models
- Practical examples and case studies
Practical AI Solutions and Projects
- Developing 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 simulations
Performance Evaluation and Optimisation
- Techniques for assessing model performance on edge hardware
- Tools for monitoring and debugging edge AI applications
- Strategies for enhancing AI model efficiency
- Addressing challenges related to latency and power consumption
Integration with IoT Systems
- Connecting Edge AI solutions with IoT devices and sensors
- Communication protocols and data exchange methods
- Building an 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
- Compliance with relevant regulations and standards
- Best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application
- Real-world projects and scenarios
- Collaborative group exercises
- Project presentations and feedback sessions
Requirements
- A solid understanding of AI and machine learning concepts
- Proficiency with programming languages (Python is recommended)
- Familiarity with the fundamentals of edge computing
Target Audience
- Developers
- Data scientists
- Tech 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
