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Course Outline
Introduction to Edge AI
- Defining Edge AI and its fundamental concepts
- Distinguishing between Edge AI and cloud-based AI
- Exploring the advantages and common use cases of Edge AI
- A broad overview of available edge devices and platforms
Configuring the Edge Environment
- Overview of key edge devices (such as Raspberry Pi and NVIDIA Jetson)
- Installation of essential software and libraries
- Setting up the development environment
- Preparing hardware for AI workload deployment
Creating AI Models for Edge Deployment
- Survey of machine learning and deep learning architectures suited for edge devices
- Methods for training models in both local and cloud settings
- Optimisation techniques for edge compatibility (including quantization and pruning)
- Utilisation of development tools and frameworks (e.g., TensorFlow Lite, OpenVINO)
Deploying AI Models on Edge Hardware
- Procedures for deploying AI models across different edge hardware types
- Executing real-time data processing and inference on edge devices
- Monitoring and managing models post-deployment
- Reviewing practical examples and industry case studies
Practical AI Solutions and Projects
- Creating AI applications for edge contexts (e.g., 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 simulating real-world scenarios
Performance Assessment and Optimisation
- Techniques for measuring model performance on edge devices
- Utilising tools for monitoring and debugging Edge AI applications
- Strategies for enhancing AI model efficiency
- Mitigating challenges related to latency and power consumption
Integration with IoT Ecosystems
- Linking Edge AI solutions with IoT devices and sensor networks
- Understanding communication protocols and data exchange mechanisms
- Constructing end-to-end Edge AI and IoT solutions
- Examples of practical integrations
Ethical and Security Implications
- Safeguarding data privacy and security in Edge AI contexts
- Mitigating bias and ensuring fairness in AI models
- Ensuring compliance with relevant regulations and standards
- Adopting best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application
- Engaging with real-world project scenarios
- Participating in collaborative group exercises
- Presenting projects and receiving constructive feedback
Requirements
- A solid understanding of AI and machine learning principles.
- Practical experience with programming languages (Python is advised).
- General familiarity with edge computing concepts.
Target Audience
- Software 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
