Become an Infra AI Expert with GPU Acceleration

The Certified Infra AI Expert: End-to-End GPU-Accelerated AI Systems Training is an extensive, hands-on program designed to empower AI engineers, developers, and system architects with expertise in the NVIDIA GPU ecosystem. This course offers a unique opportunity to learn how to build production-ready AI solutions from scratch. Whether your focus is on data center GPUs like the A100 and H100, deploying edge AI on Jetson Orin, or creating digital twins with Omniverse, this program meticulously guides you through every stage of the AI lifecycle — from model training and optimization to deployment and integration on cloud and edge environments.

Through a series of hands-on labs, participants will learn to set up GPU-powered infrastructure across various platforms including AWS, Azure, and DGX Cloud, while gaining deep insights into the NVIDIA AI Enterprise stack. You will gain practical knowledge on configuring NVIDIA drivers, managing Kubernetes GPU nodes, and utilizing Helm charts to facilitate scalable AI workloads. Additionally, the course covers workflows for NGC Registry, equipping you to deploy AI containers, leverage pretrained models, and harness tools like the NVIDIA DeepStream SDK for real-time analytics and RAPIDS for accelerated data processing.

One of the course highlights includes a Capstone Project, where you have the opportunity to design and deploy a complete AI solution utilizing NVIDIA hardware and software. Choose from exciting options such as video surveillance with DeepStream, digital twin simulation with Omniverse, or smart edge AI projects using Jetson and IoT integration. This hands-on project will not only solidify your learning but also culminate in a report, a deployment pipeline, and a demo video, showcasing your newfound skills and enhancing your professional portfolio. Upon completion of this course, you will earn the prestigious Certified NVIDIA AI Expert credential, underscoring your capabilities in designing, optimizing, and deploying sophisticated AI solutions across multiple sectors.

What you will 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.

Course Content:

• Sections: 11
• Lectures: 49
• Duration: 2h 34m

Requirements:

• Basic understanding of AI/ML concepts such as training, inference, and model deployment.
• Familiarity with Linux command-line operations (Ubuntu recommended).
• Basic knowledge of Docker and containerization (helpful but not mandatory — key concepts are covered in the course).
• Access to a GPU-enabled system (NVIDIA A100, H100, L4, or Jetson Orin/Xavier) or cloud GPU instance (AWS, Azure, DGX Cloud).
• Stable internet connection for downloading NVIDIA NGC containers, pretrained models, and SDKs.
• Curiosity and a willingness to learn hands-on through labs and real-world projects.

Who is it 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.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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