Master NVIDIA AI Infrastructure & Pass NCA-AIIO

Packt via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

6 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

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Overview

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Learn to master NVIDIA's AI infrastructure, from GPUs to ML workflows. Explore AI-centric data centers, the NVIDIA tech stack, and advanced tools like CUDA and NCCL. Prepare for the NCA-AIIO certification with practical, real-world examples to advance your skills in AI and machine learning. Mastering AI infrastructure is crucial for optimizing performance and scaling AI projects. This course covers the evolution of AI, its applications across industries, and the critical technologies behind NVIDIA’s AI stack. You’ll explore GPUs, DPUs, and network fabrics in AI-centric data centers, understanding how each component plays a role in creating efficient AI systems. Dive deep into NVIDIA hardware like the DGX platform, DGX SuperPOD, and core libraries such as CUDA and NCCL. Learn the importance of GPU virtualization techniques like vGPU and MIG, and compare key network technologies like InfiniBand and Ethernet. By the end of the course, you will have hands-on experience with NVIDIA’s tools for optimizing AI infrastructure. The course concludes by exploring AI workflows, model training, job scheduling, container orchestration, and ML Ops, with a focus on NVIDIA’s tools for managing machine learning operations. You’ll leave with the expertise to optimize AI systems and be fully prepared to pass the NCA-AIIO certification exam, a key credential for AI professionals. This course is ideal for professionals in AI infrastructure management, data center operations, machine learning, and AI system optimization. It's perfect for technical specialists eager to enhance their skills in GPU-accelerated AI technologies and ML Ops. A basic understanding of AI, machine learning, and data centers is helpful but not required, with a strong interest in NVIDIA technologies and GPU computing enhancing the learning experience. This course offers hands-on, project-based learning, allowing you to work with real-world AI infrastructure scenarios. You’ll explore NVIDIA’s hardware and software tools, mastering AI workflows and machine learning operations. With expert guidance, you’ll develop the skills needed to manage and optimize AI infrastructure, preparing you for the NCA-AIIO certification. This course is based on Master NVIDIA AI Infrastructure & Pass NCA-AIIO, by Ashish Prajapati. This Course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

Syllabus

Introduction
- This module provides an overview of the NVIDIA AI Infrastructure course, introducing key concepts and objectives that set the foundation for understanding AI systems and their applications.
Certification Details
- This module provides an in-depth look at NVIDIA's AI certification programs, covering the different types of certifications, their target audiences, and the key topics and skills assessed in the AI Infrastructure and Operations exam. Learners will gain insights into how these certifications can enhance their professional credibility and technical expertise in AI.
Module 1 – Fundamentals
- This module provides an overview of the key drivers behind AI's evolution, explores its practical applications across industries, and clarifies the distinctions between AI, machine learning, deep learning, and generative AI. It also introduces the transformer model and its impact on modern AI systems.
Module 2 - Inside an AI-Centric Data Center
- This module provides an in-depth exploration of the infrastructure and technologies that power AI-centric data centers. Learners will gain a clear understanding of key elements such as compute power, networking, storage, and energy efficiency. It also covers the comparative roles of different hardware components and deployment strategies for AI workloads.
Module 3 - NVIDIA Technology Stack
- This module provides a comprehensive overview of the NVIDIA technology stack, covering hardware, software, and networking solutions for AI infrastructure. Learners will gain insight into GPU architecture, data movement technologies, virtualization, and management tools that support AI and ML workloads.
Module 4 - AI Workflows
- This module provides a comprehensive overview of AI workflows, covering key stages like data collection, model training, and deployment. It also explores popular ML frameworks, NVIDIA's role in AI, and the importance of ML Ops in managing and scaling AI systems. Learners will gain practical insights into tools like Slurm and Kubernetes and how they support efficient AI operations.