Deploying AI Models on Edge Devices with NVIDIA Jetson Training Course
NVIDIA Jetson is a powerful platform for deploying AI models on edge devices, enabling real-time processing with high efficiency.
This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers, embedded engineers, and robotics engineers who wish to optimize and deploy AI models on NVIDIA Jetson platforms for edge applications.
By the end of this training, participants will be able to:
- Understand the fundamentals of edge AI and NVIDIA Jetson hardware.
- Optimize AI models for deployment on edge devices.
- Use TensorRT for accelerating deep learning inference.
- Deploy AI models using JetPack SDK and ONNX Runtime.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Edge AI and NVIDIA Jetson
- Overview of edge AI applications
- Introduction to NVIDIA Jetson hardware
- JetPack SDK components and development environment
Setting Up the Development Environment
- Installing JetPack SDK and setting up the Jetson board
- Understanding TensorRT and model optimization
- Configuring the runtime environment
Optimizing AI Models for Edge Deployment
- Model quantization and pruning techniques
- Using TensorRT for model acceleration
- Converting models to ONNX format
Deploying AI Models on Jetson Devices
- Running inference with TensorRT
- Integrating AI models with real-time applications
- Optimizing performance and reducing latency
Computer Vision and Deep Learning on Jetson
- Deploying image classification and object detection models
- Using AI for real-time video analytics
- Implementing AI-powered robotics applications
Edge AI Security and Performance Optimization
- Securing AI models on edge devices
- Power efficiency and thermal management
- Scaling AI applications on Jetson platforms
Project Implementation and Real-World Use Cases
- Building an AI-powered IoT solution
- Deploying AI in autonomous systems
- Case studies of AI on edge devices
Summary and Next Steps
Requirements
- Experience with AI model training and inference
- Basic knowledge of embedded systems
- Familiarity with Python programming
Audience
- AI developers
- Embedded engineers
- Robotics engineers
Open Training Courses require 5+ participants.
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