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Course Outline
Introduction to Edge AI
- Defining key concepts and terminology
- Distinguishing between Edge AI and cloud-based AI
- Exploring the benefits and typical use cases of Edge AI
- Overview of available edge devices and platforms
Establishing the Edge Environment
- Familiarization with edge devices (such as Raspberry Pi, NVIDIA Jetson, etc.)
- Installation of required software and libraries
- Configuration of the development environment
- Preparing hardware infrastructure for AI deployment
Creating AI Models for the Edge
- Overview of machine learning and deep learning models suitable for edge devices
- Methods for training models in both local and cloud environments
- Optimizing models for edge deployment (techniques like quantization and pruning)
- Utilizing tools and frameworks for Edge AI development (including TensorFlow Lite, OpenVINO, etc.)
Deploying AI Models on Edge Hardware
- Process steps for deploying AI models across various edge hardware
- Managing real-time data processing and inference on edge devices
- Monitoring and maintaining deployed models
- Reviewing practical 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: Developing a smart camera system
- Hands-on project: Implementing voice recognition on edge devices
- Collaborative group projects based on real-world scenarios
Performance Assessment and Optimization
- Methods for evaluating model performance on edge devices
- Tools for monitoring and debugging edge AI applications
- Strategies for enhancing AI model performance
- Mitigating challenges related to latency and power consumption
Integration with IoT Systems
- Linking edge AI solutions with IoT devices and sensors
- Understanding communication protocols and data exchange methods
- Constructing an end-to-end Edge AI and IoT solution
- Examining practical integration examples
Ethical and Security Considerations
- Ensuring data privacy and security in Edge AI applications
- Mitigating bias and promoting fairness in AI models
- Adhering to regulatory compliance and industry standards
- Applying best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application
- Working on real-world projects and scenarios
- Participating in collaborative group exercises
- Presenting projects and receiving feedback
Requirements
- A solid understanding of AI and machine learning concepts
- Proficiency in programming languages (Python is recommended)
- Familiarity with 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
