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Course Outline
Introduction to Edge AI
- Defining Edge AI and exploring its key concepts
- Distinguishing between Edge AI and cloud-based AI architectures
- Highlighting the benefits and primary use cases of Edge AI
- Surveying common edge devices and platforms
Setting Up the Edge Environment
- Overview of edge hardware (including Raspberry Pi, NVIDIA Jetson, etc.)
- Installing essential software dependencies and libraries
- Configuring a ready-to-use development environment
- Preparing hardware components for AI workloads
Developing AI Models for the Edge
- Examining machine learning and deep learning models suited for edge devices
- Techniques for training models in both local and cloud settings
- Applying optimization methods for edge deployment (such as quantization and pruning)
- Leveraging tools and frameworks for Edge AI (e.g., TensorFlow Lite, OpenVINO)
Deploying AI Models on Edge Devices
- Step-by-step deployment of AI models on various edge hardware configurations
- Managing real-time data processing and inference on the edge
- Techniques for monitoring and maintaining deployed models
- Analyzing practical examples and industry case studies
Practical AI Solutions and Projects
- Building AI applications for specific edge use cases (such as computer vision and NLP)
- Hands-on project: Designing a smart camera system
- Hands-on project: Implementing voice recognition on edge devices
- Collaborative group projects simulating real-world scenarios
Performance Evaluation and Optimization
- Methods for assessing model performance in edge environments
- Using tools to monitor and debug edge AI applications
- Strategies for enhancing AI model efficiency
- Mitigating challenges related to latency and power consumption
Integration with IoT Systems
- Linking Edge AI solutions with IoT devices and sensor networks
- Exploring communication protocols and data exchange mechanisms
- Constructing a complete end-to-end Edge AI and IoT ecosystem
- Reviewing practical integration examples
Ethical and Security Considerations
- Safeguarding data privacy and security within Edge AI frameworks
- Addressing bias and ensuring fairness in AI model design
- Ensuring compliance with relevant regulations and industry standards
- Adopting best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application from scratch
- Working through real-world project scenarios
- Engaging in collaborative group exercises
- Presenting projects and receiving constructive feedback
Requirements
- A foundational understanding of AI and machine learning concepts
- Proficiency in programming languages, with Python being the recommended standard
- 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
