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Course Outline
Foundations of Edge AI
- Defining key concepts and terminology
- Distinguishing between Edge AI and cloud-based AI
- Exploring the advantages and typical use cases
- Surveying available edge devices and platforms
Establishing the Edge Environment
- Overview of common edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installing required software stacks and libraries
- Configuring the developer workspace
- Preparing hardware for AI workload execution
Model Development for Edge Contexts
- Reviewing machine learning and deep learning models suitable for edge
- Methods for training models in local and cloud environments
- Optimizing models for edge efficiency (quantization, pruning, etc.)
- Utilizing key Edge AI frameworks (TensorFlow Lite, OpenVINO, etc.)
Deployment on Edge Hardware
- Process for deploying AI models across different edge devices
- Handling real-time data processing and inference
- Overseeing and managing live deployed models
- Applying practical examples and case studies
Applied AI Solutions and Projects
- Creating AI applications for edge (e.g., computer vision, NLP)
- Project: Engineering a smart camera system
- Project: Integrating voice recognition on edge devices
- Collaborative group projects simulating real-world conditions
Performance Assessment and Refinement
- Methods for evaluating model performance on edge devices
- Using tools for monitoring and debugging Edge AI apps
- Strategies for boosting AI model efficiency
- Mitigating latency and power consumption issues
IoT System Integration
- Linking edge AI solutions with IoT sensors and devices
- Managing communication protocols and data exchange
- Constructing an end-to-end Edge AI and IoT architecture
- Demonstrating practical integration workflows
Ethical and Security Frameworks
- Safeguarding data privacy and security in Edge AI
- Mitigating bias and ensuring fairness in AI models
- Adhering to regulatory standards and compliance
- Adopting best practices for responsible AI deployment
Capstone Projects and Practical Exercises
- Building a comprehensive Edge AI application
- Executing projects based on real-world scenarios
- Participating in collaborative group exercises
- Presenting projects and receiving expert feedback
Requirements
- Familiarity with core AI and machine learning concepts
- Proficiency in programming languages (Python is recommended)
- Knowledge of edge computing fundamentals
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
