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Course Outline
Fundamentals of Edge AI and NVIDIA Jetson
- Overview of edge AI application scenarios
- Introduction to NVIDIA Jetson hardware architecture
- JetPack SDK components and development setup
Configuring the Development Environment
- Installing JetPack SDK and initializing the Jetson board
- Insights into TensorRT and model optimization strategies
- Setting up the runtime configuration
Refining AI Models for Edge Deployment
- Techniques for model quantization and pruning
- Accelerating models with TensorRT
- Converting models to the ONNX format
Deploying AI Models on Jetson Hardware
- Executing inference using TensorRT
- Integrating AI models into real-time applications
- Enhancing performance and minimizing latency
Computer Vision and Deep Learning on Jetson
- Deploying image classification and object detection models
- Applying AI for real-time video analytics
- Developing AI-driven robotics applications
Edge AI Security and Performance Tuning
- Securing AI models on edge infrastructure
- Managing power efficiency and thermal constraints
- Scaling AI applications across Jetson platforms
Project Execution and Practical Applications
- Constructing an AI-powered IoT solution
- Deploying AI in autonomous systems
- Reviewing case studies of AI on edge devices
Conclusion and Future Pathways
Requirements
- Prior experience in AI model training and inference.
- Foundational knowledge of embedded systems.
- Proficiency in Python programming.
Target Audience
- AI Developers
- Embedded Engineers
- Robotics Engineers
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
