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Duration 21 hours
Course Outline
Introduction to Edge AI and NVIDIA Jetson
- Exploration of key edge AI application scenarios
- Overview of NVIDIA Jetson hardware capabilities
- Analysis of JetPack SDK components and the associated development ecosystem
Establishing the Development Environment
- Installation of JetPack SDK and configuration of the Jetson board
- In-depth look at TensorRT and strategies for model optimization
- Configuration of the runtime environment for optimal performance
Optimizing AI Models for Edge Deployment
- Application of model quantization and pruning techniques
- Leveraging TensorRT for significant model acceleration
- Process for converting models into ONNX format
Deploying AI Models on Jetson Devices
- Execution of inference workflows using TensorRT
- Integration of AI models into real-time application frameworks
- Techniques for performance tuning and latency reduction
Computer Vision and Deep Learning on Jetson
- Deployment of image classification and object detection models
- Utilizing AI for advanced real-time video analytics
- Development of AI-driven robotics applications
Edge AI Security and Performance Optimization
- Strategies for securing AI models on edge infrastructure
- Managing power efficiency and thermal constraints
- Scaling AI applications effectively across Jetson platforms
Project Implementation and Real-World Use Cases
- Construction of an AI-powered IoT solution
- Deployment of AI technologies in autonomous systems
- Review of industry case studies involving edge AI
Summary and Next Steps
Requirements
- Practical experience in training and running AI inference models.
- Foundational understanding of embedded systems.
- Proficiency in Python programming.
Intended Audience
- AI Developers
- Embedded Engineers
- Robotics Engineers
Testimonials (1)
That we can cover advance topic and work with real-life example