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Course Outline
Foundations of Edge AI
- Definitions and core conceptual frameworks
- Distinctions between Edge AI and cloud-based AI
- Key advantages and primary use cases
- Survey of current edge devices and platforms
Configuring the Edge Environment
- Introduction to edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installation of required software and libraries
- Setup of the development workspace
- Hardware preparation for AI model deployment
Model Development for the Edge
- Overview of machine learning and deep learning architectures suitable for edge devices
- Methodologies for training models in both local and cloud settings
- Optimization techniques for edge deployment (such as quantization and pruning)
- Utilization of specific tools and frameworks for Edge AI (including TensorFlow Lite, OpenVINO, and others)
Model Deployment on Edge Devices
- Procedures for deploying AI models across diverse edge hardware
- Handling real-time data processing and inference tasks
- Ongoing monitoring and management of deployed models
- Illustrative examples and detailed case studies
Practical AI Applications and Projects
- Creating AI applications for edge hardware (such as computer vision and natural language processing)
- Hands-on project: Constructing a smart camera system
- Hands-on project: Enabling voice recognition on edge devices
- Collaborative group projects simulating real-world scenarios
Performance Analysis and Refinement
- Methods for assessing model efficacy 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
IoT System Integration
- Linking Edge AI solutions with IoT devices and sensors
- Exploring communication protocols and data exchange techniques
- Designing an end-to-end Edge AI and IoT solution
- Real-world integration demonstrations
Ethics and Security
- Safeguarding data privacy and security in Edge AI contexts
- Mitigating bias and ensuring fairness in AI models
- Adhering to relevant regulations and industry standards
- Implementing best practices for responsible AI deployment
Comprehensive Hands-On Exercises
- Building a complete Edge AI application
- Tackling real-world projects and scenarios
- Participating in collaborative group exercises
- Presenting projects and receiving constructive feedback
Requirements
- A foundational understanding of AI and machine learning concepts
- Proficiency in programming languages (Python is preferred)
- Basic knowledge of 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
