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Duration 14 hours
Course Outline
Introduction to Edge AI
- Core definitions and foundational concepts
- Comparing Edge AI with cloud-based AI
- Key advantages and typical use cases
- Survey of current edge devices and platforms
Setting Up the Edge Environment
- Overview of edge hardware (such as Raspberry Pi, NVIDIA Jetson, etc.)
- Installing required software and libraries
- Configuring the development workspace
- Preparing hardware for AI deployment
Developing AI Models for the Edge
- Exploring machine learning and deep learning models suited for edge devices
- Methods for training models in local and cloud settings
- Optimizing models for edge deployment (including quantization and pruning)
- Utilizing frameworks for Edge AI (such as TensorFlow Lite, OpenVINO, etc.)
Deploying AI Models on Edge Devices
- Processes for deploying AI models across various edge hardware
- Handling real-time data processing and inference
- Monitoring and managing deployed models
- Reviewing practical examples and case studies
Practical AI Solutions and Projects
- Creating AI applications for edge devices (e.g., computer vision, natural language processing)
- Hands-on project: Constructing a smart camera system
- Hands-on project: Implementing voice recognition on edge devices
- Group collaborative projects and real-world simulations
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 latency and power consumption issues
Integration with IoT Systems
- Linking Edge AI solutions with IoT devices and sensors
- Understanding communication protocols and data exchange
- Developing an end-to-end Edge AI and IoT solution
- Practical integration walkthroughs
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 standards
- Best practices for responsible AI deployment
Hands-On Projects and Exercises
- Building a comprehensive Edge AI application
- Working through real-world projects and scenarios
- Participating in collaborative group exercises
- Presenting projects and receiving feedback
Requirements
- A solid grasp of AI and machine learning fundamentals
- Proficiency in programming languages, with Python being recommended
- Basic knowledge of edge computing concepts
Target Audience
- Software Developers
- Data Scientists
- Tech 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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