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Duration 21 hours
Course Outline
Introduction to Edge AI and the NVIDIA Jetson Ecosystem
- Exploring the scope and impact of edge AI applications.
- A detailed look at NVIDIA Jetson hardware capabilities.
- Navigating JetPack SDK components and establishing the development workspace.
Preparing the Development Environment
- Installing the JetPack SDK and configuring the Jetson board for development.
- Gaining insights into TensorRT functionality and model optimization strategies.
- Fine-tuning the runtime environment for optimal performance.
Refining AI Models for Edge Readiness
- Applying model quantization and pruning methods to reduce size.
- Utilizing TensorRT to boost model execution speed.
- The process of converting models into the ONNX format.
Executing AI Model Deployment on Jetson Hardware
- Performing inference operations using TensorRT.
- Seamlessly integrating AI models into real-time application workflows.
- Strategies for enhancing performance and minimizing latency.
Advancing Computer Vision and Deep Learning on Jetson
- Deploying models for image classification and object detection tasks.
- Applying AI for real-time video analytics.
- Developing AI-driven robotics applications.
Enhancing Edge AI Security and Performance
- Implementing security measures for AI models on edge devices.
- Managing power efficiency and thermal performance.
- Techniques for scaling AI applications across Jetson platforms.
Project Execution and Practical Applications
- Constructing an AI-enabled IoT solution.
- Integrating AI into autonomous systems.
- Reviewing case studies of AI implementation on edge devices.
Conclusion and Future Directions
Requirements
- Practical experience in training and running AI model inference.
- Foundational understanding of embedded systems.
- Proficiency in Python programming.
Target Audience
- AI developers.
- Embedded systems engineers.
- Robotics engineers.
Testimonials (1)
That we can cover advance topic and work with real-life example
