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Course Outline
Foundations of Edge AI
- Definitions and essential concepts
- Distinguishing Edge AI from cloud-based AI
- Advantages and typical use cases
- Overview of available edge devices and platforms
Configuring the Edge Environment
- Introduction to edge hardware (such as Raspberry Pi, NVIDIA Jetson, etc.)
- Installing required software and libraries
- Setting up the development workspace
- Preparing hardware for AI deployment
Creating AI Models for Edge Devices
- Survey of machine learning and deep learning architectures suitable for edge
- Methods for training models in local and cloud environments
- Optimization techniques for edge deployment (quantization, pruning, etc.)
- Key tools and frameworks for Edge AI (TensorFlow Lite, OpenVINO, etc.)
Deploying AI Models onto Edge Hardware
- Procedures for deploying models across different edge hardware
- Managing real-time data processing and inference
- Monitoring and maintaining deployed models
- Real-world examples and case studies
Practical AI Solutions and Projects
- Building 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 based on real-world scenarios
Assessing and Optimizing Performance
- Methods for benchmarking model performance on edge devices
- Tools for monitoring and debugging Edge AI applications
- Strategies to enhance AI model efficiency
- Overcoming latency and power consumption constraints
Integration with IoT Systems
- Linking Edge AI solutions with IoT devices and sensors
- Communication protocols and data exchange mechanisms
- Designing end-to-end Edge AI and IoT architectures
- Practical integration examples
Ethics and Security in Edge AI
- Safeguarding data privacy and security in Edge AI contexts
- Mitigating bias and ensuring fairness in AI models
- Adhering to regulatory and industry standards
- Best practices for responsible AI deployment
Capstone Projects and Exercises
- Developing a comprehensive Edge AI application
- Working on real-world projects and scenarios
- Collaborative group exercises
- Project presentations and constructive feedback
Requirements
- Foundational knowledge of AI and machine learning concepts
- Proficiency in programming languages (Python is recommended)
- Basic understanding of edge computing principles
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
