Become a Certified Infra AI Expert with GPU Acceleration

The Certified Infra AI Expert: End-to-End GPU-Accelerated AI course is a comprehensive and hands-on training program designed for AI engineers, developers, and system architects. This course enables learners to master the NVIDIA GPU ecosystem and construct production-ready AI solutions from the ground up. Participants will engage with a variety of tools and platforms, including data center GPUs like the A100 and H100, as well as edge AI solutions using Jetson Orin. Each phase of the AI lifecycle is covered in detail, from model training and optimization to deployment and integration in cloud and edge environments.

Throughout this course, you will gain a profound understanding of the NVIDIA AI Enterprise stack, learning to set up GPU-powered infrastructures on major cloud platforms such as AWS, Azure, and DGX Cloud. Detailed labs will guide you through configuring NVIDIA drivers, deploying Kubernetes GPU nodes, and utilizing Helm charts for scalable AI workloads. Additionally, the course discusses workflows with the NGC Registry, focusing on deploying AI containers, using pretrained models, and integrating NVIDIA DeepStream SDK for real-time video analytics.

The training culminates in a Capstone Project where you will design and deploy a complete AI solution using NVIDIA technologies. You will have options to work on video surveillance with DeepStream, digital twin simulation with Omniverse, or smart edge AI projects involving Jetson and IoT sensor fusion. By the end of the course, you will be equipped with the skills and credentials to excel as a professional in the AI field, bridging the gap between research and practical implementation across various industries.

What you will learn:

• 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

Course Content:

• Sections: 10
• Lectures: 50
• Duration: 30 hours

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).
• 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.

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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