Name: Certified NVIDIA AI Expert: End-to-End GPU-Accelerated AI
Availability: InStock

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.

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.

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.

AI Specialist | IT Professional Trainer | Principal Engineer

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81,304 Students

6 Courses

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