Table of Contents
What you’ll learn
- Architect and deploy GPU-accelerated AI pipelines using NVIDIA hardware (A100, H100, L4, Jetson) and the full NVIDIA AI Enterprise software stack.
- Optimize AI models for performance and efficiency using TensorRT, TAO Toolkit, and advanced quantization techniques for both cloud and edge deployments.
- Implement real-time AI applications with DeepStream, RAPIDS, and Triton Inference Server for video analytics, sensor fusion, and data processing.
- Integrate AI solutions with cloud, edge, and digital twin environments, leveraging Kubernetes, Helm, and Omniverse for scalable deployment and simulation.
- Apply security, licensing, and containerization best practices to ensure enterprise-grade reliability and compliance in AI systems.
Description
The Certified NVIDIA AI Expert: End-to-End GPU-Accelerated AI Systems Training is a comprehensive, hands-on program designed for AI engineers, developers, and system architects who want to master the NVIDIA GPU ecosystem and build production-ready AI solutions from the ground up. Whether you’re working with data center GPUs like the A100 and H100, deploying edge AI on Jetson Orin, or developing digital twins with Omniverse, this course takes you through every stage of the AI lifecycle — from model training to optimization, deployment, and cloud/edge integration.
You’ll gain deep expertise in the NVIDIA AI Enterprise stack, learning how to set up GPU-powered infrastructure on AWS, Azure, and DGX Cloud. Through step-by-step labs, you’ll configure NVIDIA drivers, Kubernetes GPU nodes, and Helm charts for scalable AI workloads. The course covers NGC Registry workflows, showing you how to deploy AI containers, use pretrained models, and integrate NVIDIA DeepStream SDK for real-time video analytics and RAPIDS for GPU-accelerated data processing.
We’ll dive into NVIDIA Triton Inference Server for high-throughput inference, TAO Toolkit for transfer learning and quantization, and TensorRT for model optimization. You’ll learn best practices for container security, licensing via NVIDIA License Server, and cloud-native AI DevOps using Kubernetes, Helm, and CI/CD pipelines.
Specialized modules explore NVIDIA vertical SDKs such as:
- Metropolis for smart cities
- Riva for speech AI
- NeMo for NLP
- Clara for healthcare AI
- Merlin for recommender systems
A highlight of the training is the Capstone Project, where you’ll design and deploy a complete AI solution using NVIDIA hardware and software. Choose between:
- Video surveillance with DeepStream
- Digital twin simulation with Omniverse
- Smart edge AI with Jetson and IoT sensor fusion
You’ll integrate TensorRT optimization, Triton inference, and cloud-edge synchronization, delivering a project report, deployment pipeline, and demo video — essential portfolio pieces for demonstrating your skills.
By the end of this course, you will be able to:
- Architect GPU-accelerated AI pipelines from data ingestion to deployment
- Implement real-time AI systems with DeepStream, RAPIDS, and Triton
- Optimize AI models for performance and efficiency using TensorRT
- Deploy scalable AI solutions on cloud platforms and edge devices
- Integrate AI with digital twins, IoT sensors, and streaming pipelines
- Apply security and licensing best practices for enterprise AI environments
Upon successful completion, you’ll earn the Certified NVIDIA AI Expert credential, validating your ability to design, optimize, and deploy AI solutions using the full NVIDIA technology stack. This certification sets you apart as a professional who can bridge AI research and real-world implementation, making you highly valuable in industries from autonomous systems to healthcare, finance, manufacturing, and beyond.
If your goal is to become an end-to-end AI solutions architect with cutting-edge GPU acceleration skills, this is the definitive NVIDIA AI training program to get you there.
Who this course is for:
- AI/ML Developers looking to move beyond model training into real-world deployment and optimization on NVIDIA hardware.
- Edge AI Engineers working with Jetson devices and IoT sensor integration for real-time applications.
- System Architects and DevOps Engineers responsible for cloud-native AI infrastructure, Kubernetes orchestration, and containerized AI workloads.
- Technical Product Managers and Solution Engineers who need a deep, hands-on understanding of NVIDIA AI Enterprise, DeepStream, RAPIDS, Triton, and Omniverse.
- Researchers aiming to deploy optimized AI pipelines in high-performance computing or industry-specific environments like healthcare, smart cities, robotics, or manufacturing.
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