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
- Definition and core concepts
- Key distinctions between Edge AI and cloud-based AI
- Advantages and primary use cases of Edge AI
- Overview of common edge devices and platforms
Configuring the Edge Environment
- Introduction to edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installing required software and libraries
- Setting up the development workspace
- Preparing hardware for AI model deployment
Developing AI Models for the Edge
- Overview of machine learning and deep learning architectures suited for edge
- Methods for training models in both local and cloud settings
- Optimizing models for edge use (techniques like quantization and pruning)
- Key tools and frameworks for Edge AI (e.g., TensorFlow Lite, OpenVINO)
Deploying AI Models on Edge Devices
- Process for deploying AI models onto various edge hardware
- Handling real-time data processing and inference on edge
- Monitoring and managing models after deployment
- Review of practical examples and case studies
Practical AI Solutions and Projects
- Building AI applications for edge (such as computer vision or NLP)
- Hands-on project: Creating a smart camera system
- Hands-on project: Integrating voice recognition on edge devices
- Collaborative group projects simulating real-world scenarios
Performance Evaluation and Optimization
- Methods for assessing model performance on edge hardware
- Using tools to monitor and debug Edge AI applications
- Strategies for enhancing AI model efficiency
- Mitigating issues related to latency and power consumption
Integration with IoT Systems
- Linking Edge AI solutions with IoT devices and sensors
- Understanding communication protocols and data exchange
- Constructing end-to-end Edge AI and IoT solutions
- Practical examples of system integration
Ethical and Security Considerations
- Safeguarding data privacy and security in Edge AI
- Mitigating bias and ensuring fairness in AI models
- Adhering to relevant regulations and industry 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
- Presenting projects and receiving feedback
Requirements
- A solid understanding of AI and machine learning principles
- Proficiency in programming languages (Python is highly recommended)
- General familiarity with the concepts of edge computing
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
