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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 primary use cases for Edge AI
- Survey of available edge devices and platforms
Configuring the Edge Environment
- Introduction to edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installation of required software components and libraries
- Setting up the development workspace
- Preparing hardware infrastructure for AI deployment
Creating AI Models for Edge Use
- Overview of machine learning and deep learning architectures suited for edge devices
- Methods for training models in both local and cloud environments
- Optimizing models for edge constraints (including quantization and pruning)
- Utilization of Edge AI development tools and frameworks (such as TensorFlow Lite and OpenVINO)
Deploying AI Models onto Edge Devices
- Methodologies for deploying models across various edge hardware platforms
- Managing real-time data processing and inference at the edge
- Monitoring and maintaining deployed models
- Analysis of practical examples and industry case studies
Practical AI Solutions and Projects
- Building AI applications for edge hardware (e.g., computer vision and NLP tasks)
- Project: Constructing an intelligent camera system
- Project: Deploying voice recognition on edge devices
- Collaborative group projects simulating real-world scenarios
Assessing and Optimizing Performance
- Techniques for benchmarking 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 Ecosystems
- Linking Edge AI solutions with IoT devices and sensor networks
- Exploring communication protocols and data exchange mechanisms
- Designing end-to-end Edge AI and IoT solutions
- Examining practical integration examples
Ethical and Security Implications
- Safeguarding data privacy and security within Edge AI frameworks
- 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 Edge AI application
- Working through real-world projects and scenarios
- Engaging in collaborative group exercises
- Presenting projects and receiving constructive feedback
Requirements
- A solid grasp of AI and machine learning fundamentals
- Proficiency in programming languages (Python is highly recommended)
- A basic understanding of edge computing principles
Target Audience
- Software 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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Working from first principles in a focused way, and moving to applying case studies within the same day
