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Course Outline
Foundations of Edge AI
- Definitions and core concepts
- Distinctions between Edge AI and Cloud AI
- Advantages and primary use cases of Edge AI
- Survey of common edge devices and platforms
Configuring the Edge Environment
- Familiarization with edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installation of required software and libraries
- Setup of the development environment
- Hardware preparation for AI deployment
Building AI Models for Edge
- Overview of machine learning and deep learning models suited for edge devices
- Methods for training models in both local and cloud settings
- Optimization strategies for edge deployment (quantization, pruning, etc.)
- Relevant tools and frameworks (TensorFlow Lite, OpenVINO, etc.)
Deployment on Edge Hardware
- Procedures for deploying AI models across different edge hardware
- Real-time data processing and inference on edge devices
- Monitoring and administration of deployed models
- Analysis of practical examples and case studies
Practical Applications and Projects
- Creating AI applications for edge devices (e.g., computer vision, NLP)
- Hands-on project: Constructing a smart camera system
- Hands-on project: Implementing voice recognition on edge hardware
- Collaborative group projects simulating real-world scenarios
Assessing and Optimizing Performance
- Techniques for evaluating model efficacy on edge devices
- Tools for monitoring and debugging Edge AI applications
- Strategies for enhancing AI model performance
- Mitigating issues related to latency and power consumption
Integration with IoT Ecosystems
- Linking Edge AI solutions with IoT devices and sensors
- Communication protocols and data exchange techniques
- Constructing a complete Edge AI and IoT solution
- Practical integration demonstrations
Ethics and Security in Edge AI
- Safeguarding data privacy and security in Edge AI applications
- Mitigating bias and ensuring fairness in AI models
- Adhering to regulatory standards and compliance
- Best practices for responsible AI deployment
Hands-On Projects and Exercises
- Development of a comprehensive Edge AI application
- Execution of real-world projects and scenarios
- Collaborative group exercises
- Project presentations and peer feedback
Requirements
- Foundational knowledge of AI and machine learning concepts
- Proficiency in programming languages (Python is preferred)
- Working knowledge of edge computing principles
Intended Audience
- Software 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
