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Duration 14 hours
Course Outline
Introduction to Edge AI
- Core definitions and fundamental concepts
- Key distinctions between Edge AI and cloud-based AI
- Advantages and specific use cases for Edge AI
- Overview of common edge devices and platforms
Setting Up the Edge Environment
- Exploring edge hardware such as Raspberry Pi and NVIDIA Jetson
- Installing required software and libraries
- Configuring the development workspace
- Preparing hardware components for AI implementation
Developing AI Models for the Edge
- Reviewing machine learning and deep learning architectures suited for edge devices
- Strategies for training models in both local and cloud settings
- Optimization techniques for edge deployment, including quantization and pruning
- Utilizing key tools and frameworks like TensorFlow Lite and OpenVINO
Deploying AI Models on Edge Devices
- Procedures for deploying models across various edge hardware types
- Managing real-time data processing and inference tasks
- Monitoring and maintaining active model deployments
- Analysis of practical examples and industry case studies
Practical AI Solutions and Projects
- Creating AI applications for edge devices, covering computer vision and natural language processing
- Hands-on project: Constructing a smart camera system
- Hands-on project: Integrating voice recognition on edge devices
- Collaborative group projects based on 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 to optimize AI model efficiency
- Mitigating latency and power consumption issues
Integration with IoT Systems
- Linking Edge AI solutions with IoT devices and sensors
- Understanding communication protocols and data exchange mechanisms
- Constructing comprehensive End-to-Edge AI and IoT architectures
- Demonstrating practical integration examples
Ethical and Security Considerations
- Safeguarding data privacy and security within Edge AI systems
- Mitigating bias and ensuring fairness in AI models
- Adhering to relevant regulations and industry standards
- Implementing best practices for responsible AI deployment
Hands-On Projects and Exercises
- Building a comprehensive Edge AI application
- Engaging with real-world projects and scenarios
- Participating in collaborative group exercises
- Presenting projects and receiving professional feedback
Requirements
- A solid understanding of AI and machine learning concepts
- Proficiency in programming languages (Python is highly recommended)
- Awareness of edge computing principles
Target Audience
- Developers
- Data scientists
- Technology enthusiasts
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
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