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Course Outline
Foundations of Edge AI
- Core definitions and concepts
- Distinguishing between Edge AI and cloud-based AI
- Advantages and specific use cases for Edge AI
- Introduction to various edge devices and platforms
Preparing the Edge Environment
- Overview of edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installation of required software and libraries
- Configuration of the development workspace
- Hardware preparation for AI model deployment
Creating AI Models for Edge Use
- Survey of machine learning and deep learning models suited for edge devices
- Methods for training models in local and cloud settings
- Optimization techniques for edge deployment (such as quantization and pruning)
- Tools and frameworks for Edge AI development (e.g., TensorFlow Lite, OpenVINO)
Deploying AI on Edge Devices
- Process for deploying AI models across different edge hardware
- Handling real-time data processing and inference on edge devices
- Techniques for monitoring and managing live models
- Review of practical examples and case studies
Implementing Practical AI Solutions
- Creating AI applications for edge hardware (e.g., computer vision, NLP)
- Hands-on project: Constructing a smart camera system
- Hands-on project: Enabling voice recognition on edge devices
- Group projects simulating real-world scenarios
Performance Analysis and Optimization
- Methods for assessing model performance on edge hardware
- Utilities for monitoring and debugging edge AI applications
- Strategies to enhance AI model efficiency
- Mitigating issues related to latency and power consumption
IoT System Integration
- Linking Edge AI solutions with IoT devices and sensors
- Understanding communication protocols and data exchange mechanisms
- Constructing a complete End-to-End Edge AI and IoT solution
- 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
- Adhering to relevant regulations and standards
- Best practices for responsible AI deployment
Applied Projects and Practice
- Building a comprehensive Edge AI application
- Engaging with real-world projects and scenarios
- Participating in collaborative group exercises
- Presenting projects and receiving constructive feedback
Requirements
- Familiarity with fundamental AI and machine learning concepts
- Proficiency in programming languages (Python is preferred)
- Basic knowledge 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
