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Course Outline
Foundations of Edge AI
- Definitions and core concepts
- Distinguishing Edge AI from Cloud AI
- Advantages and typical use cases of Edge AI
- Survey of edge devices and available platforms
Configuring the Edge Environment
- Familiarity with edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installation of required software and libraries
- Setup of the development environment
- Preparation of hardware for AI workloads
Building AI Models for Edge
- Review of machine learning and deep learning models suitable for edge
- Methods for training models in local and cloud settings
- Optimizing models for edge constraints (e.g., quantization, pruning)
- Key tools and frameworks for Edge AI (e.g., TensorFlow Lite, OpenVINO)
Deploying AI on Edge Hardware
- Procedures for deploying models across different edge devices
- Handling real-time data processing and inference on the edge
- Oversight and management of live models
- Case studies and practical demonstrations
Applied AI Solutions and Projects
- Creating AI apps for edge devices (e.g., computer vision, NLP)
- Practical exercise: Constructing a smart camera system
- Practical exercise: Deploying voice recognition on edge devices
- Team-based projects addressing real-world scenarios
Performance Assessment and Tuning
- Methods for assessing model performance on edge hardware
- Utilizing tools for monitoring and troubleshooting Edge AI apps
- Approaches to improving AI model efficiency
- Mitigating latency and energy consumption issues
Connecting with IoT Ecosystems
- Integrating Edge AI with IoT devices and sensors
- Exploring communication protocols and data exchange techniques
- Constructing a complete Edge AI and IoT workflow
- 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 regulatory frameworks and industry standards
- Best practices for ethical AI deployment
Practical Projects and Exercises
- Developing a holistic Edge AI application
- Engaging with real-world project scenarios
- Collaborative group activities
- Project showcases and review feedback
Requirements
- A solid grasp of AI and machine learning fundamentals
- Proficiency in programming languages (Python is preferred)
- Knowledge of edge computing principles
Target Audience
- 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
