Course Outline
Introduction to Edge AI
- Definition and key concepts.
- Distinguishing features between Edge AI and cloud-based AI.
- Benefits and practical use cases of Edge AI.
- Overview of edge devices and platforms.
Setting Up the Edge Environment
- Introduction to edge devices (e.g., Raspberry Pi, NVIDIA Jetson).
- Installation of required software and libraries.
- Configuration of the development environment.
- Preparing hardware for AI deployment.
Developing AI Models for the Edge
- Overview of machine learning and deep learning models suitable for edge devices.
- Techniques for training models in local and cloud environments.
- Model optimization techniques for edge deployment (quantization, pruning, etc.).
- Tools and frameworks for Edge AI development (TensorFlow Lite, OpenVINO, etc.).
Deploying AI Models on Edge Devices
- Steps for deploying AI models across various edge hardware platforms.
- 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 scenarios.
Performance Evaluation and Optimization
- Techniques for evaluating model performance on edge devices.
- Tools for monitoring and debugging edge AI applications.
- Strategies for optimizing AI model performance.
- 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
- Ensuring data privacy and security in Edge AI applications.
- Addressing bias and fairness in AI models.
- Compliance with 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.
Summary and Next Steps
Requirements
- A foundational understanding of AI and machine learning concepts.
- Proficiency in programming languages (Python is recommended).
- Familiarity with edge computing principles.
Target Audience
- Software Developers.
- Data Scientists.
- Technology Enthusiasts.
Testimonials (2)
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
