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Course Outline
Introduction to Edge AI
- Core definitions and fundamental concepts.
- Distinguishing between Edge AI and cloud-based AI.
- Key benefits and common use cases for Edge AI.
- An overview of available edge devices and platforms.
Setting Up the Edge Environment
- Familiarizing with edge hardware (e.g., Raspberry Pi, NVIDIA Jetson).
- Installing essential software components and libraries.
- Configuring the development workspace effectively.
- Preparing hardware for optimal AI deployment.
Developing AI Models for the Edge
- Exploring machine learning and deep learning models suited for edge devices.
- Methods for training models in both local and cloud environments.
- Optimizing models for edge constraints (including quantization and pruning).
- Utilizing key tools and frameworks such as TensorFlow Lite and OpenVINO.
Deploying AI Models on Edge Devices
- Step-by-step deployment processes for various edge hardware types.
- Handling real-time data processing and inference on edge.
- Techniques for monitoring and managing live models.
- Reviewing practical examples and industry case studies.
Practical AI Solutions and Projects
- Building AI applications for edge devices (e.g., computer vision, NLP).
- Project: Constructing a smart camera system.
- Project: Implementing voice recognition capabilities on edge hardware.
- Collaborative group projects simulating real-world scenarios.
Performance Evaluation and Optimization
- Methodologies for assessing model performance on edge devices.
- 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
- Connecting Edge AI solutions with IoT devices and sensors.
- Understanding communication protocols and data exchange methods.
- Constructing a complete end-to-end Edge AI and IoT solution.
- Examining practical integration examples.
Ethical and Security Considerations
- Safeguarding data privacy and security in Edge AI contexts.
- Mitigating bias and ensuring fairness in AI models.
- Ensuring compliance with relevant regulations and standards.
- Adopting best practices for responsible AI deployment.
Hands-On Projects and Exercises
- Developing a comprehensive, functional Edge AI application.
- Working on projects that reflect real-world challenges.
- Engaging in collaborative group exercises.
- Presenting projects and receiving constructive feedback.
Requirements
- A solid grasp of fundamental AI and machine learning concepts.
- Proficiency in programming languages, with a strong recommendation for Python.
- 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
