Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Edge AI
- Definitions and core concepts
- Distinguishing between Edge AI and cloud-based AI
- Advantages and typical use cases of Edge AI
- Overview of available edge devices and platforms
Setting Up the Edge Environment
- Introduction to edge devices such as Raspberry Pi and NVIDIA Jetson
- Installing required software and libraries
- Configuring the development environment
- Preparing hardware for AI deployment
Developing AI Models for the Edge
- Overview of machine learning and deep learning models suitable for edge devices
- Techniques for training models in both local and cloud environments
- Model optimization strategies for edge deployment, including quantization and pruning
- Tools and frameworks for Edge AI development, such as TensorFlow Lite and OpenVINO
Deploying AI Models on Edge Devices
- Procedures for deploying AI models on various edge hardware
- Real-time data processing and inference on edge devices
- Monitoring and managing deployed models
- Practical examples and case studies
Practical AI Solutions and Projects
- Developing AI applications for edge devices, including computer vision and natural language processing
- Hands-on project: Building a smart camera system
- Hands-on project: Implementing voice recognition on edge devices
- Collaborative group projects and real-world scenarios
Performance Evaluation and Optimization
- Methods for evaluating model performance on edge devices
- Tools for monitoring and debugging edge AI applications
- Strategies to optimize AI model performance
- Managing latency and power consumption challenges
Integration with IoT Systems
- Connecting edge AI solutions with IoT devices and sensors
- Communication protocols and data exchange methods
- Building an end-to-end Edge AI and IoT solution
- Practical integration examples
Ethical and Security Considerations
- Ensuring data privacy and security in Edge AI applications
- Mitigating bias and ensuring fairness in AI models
- Compliance with relevant regulations and standards
- Best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application
- Working on real-world projects and scenarios
- Participating in collaborative group exercises
- Project presentations and feedback sessions
Requirements
- A solid understanding of AI and machine learning concepts
- Proficiency in programming languages (Python is recommended)
- Basic familiarity with edge computing concepts
Target Audience
- 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
