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Duration 14 hours
Course Outline
Introduction to Edge AI
- Core definitions and foundational concepts
- Distinguishing between Edge AI and cloud-based AI
- Advantages and practical use cases for Edge AI
- Overview of available edge devices and platforms
Establishing the Edge Environment
- Introduction to edge hardware (e.g., Raspberry Pi, NVIDIA Jetson)
- Installation of required software and libraries
- Configuration of the development environment
- Preparation of hardware for AI deployment
Engineering AI Models for the Edge
- Overview of machine learning and deep learning models suited for edge devices
- Methods for training models in both local and cloud environments
- Optimization techniques for edge deployment (including quantization and pruning)
- Essential tools and frameworks for Edge AI development (such as TensorFlow Lite and OpenVINO)
Deploying AI Models on Edge Hardware
- Processes for deploying AI models across various edge hardware types
- Handling real-time data processing and inference on edge devices
- Monitoring and managing deployed models
- Review of 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
- Collaborative group projects reflecting real-world scenarios
Performance Assessment and Optimization
- Techniques for evaluating model performance on edge devices
- Tools for monitoring and debugging edge AI applications
- Strategies for enhancing AI model performance
- Mitigating latency and power consumption challenges
Integration with IoT Ecosystems
- Connecting edge AI solutions with IoT devices and sensors
- Communication protocols and data exchange mechanisms
- Building end-to-end Edge AI and IoT solutions
- Practical integration examples
Ethical and Security Considerations
- Safeguarding data privacy and security in Edge AI applications
- Mitigating bias and ensuring fairness in AI models
- Adhering to regulatory and industry standards
- Best practices for responsible AI deployment
Hands-On Projects and Exercises
- Developing a comprehensive Edge AI application
- Engaging in 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 a strong recommendation for Python).
- Working familiarity with edge computing concepts.
Target Audience
- 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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