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Course Outline
Introduction to Edge AI
- Defining key concepts and terminology.
- Distinguishing between Edge AI and cloud-based AI.
- Exploring the benefits and primary use cases of Edge AI.
- Surveying current edge devices and available platforms.
Setting Up the Edge Environment
- Getting acquainted with edge devices such as Raspberry Pi and NVIDIA Jetson.
- Installing the essential software components and libraries.
- Configuring the optimal development environment.
- Preparing the hardware infrastructure for AI deployment.
Developing AI Models for the Edge
- Reviewing machine learning and deep learning architectures suitable for edge devices.
- Techniques for training models in both local and cloud environments.
- Optimizing models for edge constraints (e.g., quantization, pruning).
- Utilizing key tools and frameworks for Edge AI development (e.g., TensorFlow Lite, OpenVINO).
Deploying AI Models on Edge Devices
- Executing the deployment process across various edge hardware types.
- Handling real-time data processing and inference directly on edge devices.
- Monitoring and managing active model deployments.
- Analyzing practical examples and detailed case studies.
Practical AI Solutions and Projects
- Building AI applications for edge devices (e.g., computer vision, natural language processing).
- Hands-on project: Constructing a smart camera system.
- Hands-on project: Implementing voice recognition on edge devices.
- Engaging in collaborative group projects based on real-world scenarios.
Performance Evaluation and Optimization
- Applying techniques to assess model performance specifically on edge devices.
- Using tools for monitoring and debugging Edge AI applications.
- Implementing strategies to optimize overall AI model performance.
- Mitigating challenges related to latency and power consumption.
Integration with IoT Systems
- Linking Edge AI solutions with IoT devices and sensor networks.
- Utilizing communication protocols and effective data exchange methods.
- Constructing a comprehensive, end-to-end Edge AI and IoT solution.
- Examining practical integration examples.
Ethical and Security Considerations
- Ensuring robust data privacy and security within Edge AI applications.
- Addressing issues of bias and fairness in AI model design.
- Maintaining compliance with relevant regulations and industry standards.
- Adhering to best practices for responsible AI deployment.
Hands-On Projects and Exercises
- Developing a comprehensive, full-stack Edge AI application.
- Working through real-world projects and complex scenarios.
- Participating in collaborative group exercises.
- Presenting projects and receiving constructive feedback.
Requirements
- A solid understanding of AI and machine learning fundamentals.
- Practical experience with programming languages, with a strong recommendation for Python.
- Basic familiarity with edge computing concepts.
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
