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Course Outline
Introduction to Edge AI
- Defining Edge AI and its core concepts
- Distinguishing Edge AI from Cloud AI
- Exploring the benefits and primary use cases of Edge AI
- Overview of popular edge devices and platforms
Configuring the Edge Environment
- Introduction to edge hardware such as Raspberry Pi and NVIDIA Jetson
- Installing essential software and libraries
- Setting up the development environment
- Preparing hardware for AI model deployment
Developing AI Models for Edge Deployment
- Overview of machine learning and deep learning architectures suitable for edge devices
- Methods for training models in both local and cloud environments
- Optimization techniques for edge deployment, including quantization and pruning
- Introduction to key tools and frameworks for Edge AI development (e.g., TensorFlow Lite, OpenVINO)
Deploying AI Models on Edge Hardware
- Procedures for deploying AI models across various edge hardware platforms
- Executing real-time data processing and inference on edge devices
- Strategies for monitoring and managing deployed models
- Review of practical examples and industry case studies
Practical AI Solutions and Projects
- Creating AI applications for edge devices, such as computer vision and natural language processing
- Hands-on project: Constructing a smart camera system
- Hands-on project: Implementing voice recognition on edge devices
- Collaborative group projects focused on real-world scenarios
Performance Evaluation and Optimization
- Methods for assessing model performance on edge devices
- Utilizing tools for monitoring and debugging Edge AI applications
- Strategies to optimize AI model performance
- Mitigating challenges related to latency and power consumption
Integration with IoT Systems
- Connecting Edge AI solutions with IoT devices and sensors
- Understanding communication protocols and data exchange methods
- Architecting end-to-end Edge AI and IoT solutions
- Practical integration examples
Ethical and Security Considerations
- Safeguarding data privacy and security within Edge AI applications
- 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
- Building a comprehensive Edge AI application
- Engaging in real-world projects and scenarios
- Participating in collaborative group exercises
- Presenting projects and receiving constructive feedback
Requirements
- Foundational knowledge of AI and machine learning concepts
- Proficiency in programming languages (with a recommendation for Python)
- Basic familiarity with edge computing principles
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
