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Course Outline
Foundations of Edge AI
- Core definitions and essential concepts
- Distinguishing Edge AI from cloud-based AI
- Advantages and practical use cases of Edge AI
- Survey of common edge devices and platforms
Configuring the Edge Environment
- Overview of edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installing required software and libraries
- Setting up the development workspace
- Preparing hardware for AI deployment
Engineering AI Models for Edge
- Survey of machine learning and deep learning models suitable for edge
- Methods for training models in local and cloud settings
- Optimization techniques for edge deployment (quantization, pruning, etc.)
- Key tools and frameworks for Edge AI (TensorFlow Lite, OpenVINO, etc.)
Deploying AI on Edge Hardware
- Procedures for deploying models across various edge devices
- Real-time data processing and inference at the edge
- Monitoring and management of deployed models
- Practical examples and case studies
Practical AI Applications and Projects
- Building 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 based on real-world scenarios
Performance Assessment and Tuning
- Methods for evaluating model performance on edge devices
- Tools for monitoring and debugging Edge AI applications
- Strategies for enhancing AI model efficiency
- Mitigating latency and power consumption issues
Integration with IoT Ecosystems
- Linking Edge AI solutions with IoT devices and sensors
- Communication protocols and data exchange mechanisms
- Constructing a complete Edge AI and IoT solution
- Practical integration examples
Ethical and Security Frameworks
- Ensuring data privacy and security in Edge AI contexts
- Mitigating bias and ensuring fairness in AI models
- Adhering to regulatory standards and compliance
- Best practices for responsible AI deployment
Capstone Projects and Exercises
- Developing a comprehensive Edge AI application
- Applying skills to real-world projects and scenarios
- Collaborative group exercises
- Project presentations and constructive feedback
Requirements
- A solid grasp of artificial intelligence and machine learning concepts.
- Proficiency in programming languages (Python is highly recommended).
- A basic familiarity with edge computing principles.
Target 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
