NVIDIA
AI Infrastructure and Operations Fundamentals
NVIDIA

AI Infrastructure and Operations Fundamentals

Taught in English

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18,630 already enrolled

Course

Gain insight into a topic and learn the fundamentals

NVIDIA Training

Instructor: NVIDIA Training

4.5

(102 reviews)

Beginner level
No prior experience required
8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Explore diverse applications of AI across various industries. Understand concepts like Machine Learning, Deep Leaning, training and inference.

  • Trace the evolution of AI Technologies. From its inception to the revolutionary advances brought by Generative AI, and the role of GPUs.

  • You will become familiar with deep learning frameworks and AI software stack.

  • Learn about considerations when deploying AI workloads on a data center on prem, in the cloud, on a hybrid model, or on a multi-cloud environment.

Details to know

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Assessments

21 assignments

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There are 4 modules in this course

In this module, you will explore AI applications across various industries and delve into fundamental concepts of AI, Machine Learning (ML), and Deep Learning (DL). Additionally, the course will introduce you to Generative AI, how Large Language Models (LLMs) work and new business opportunities being unlocked with this new technology. You will understand what a GPU is, distinguish the key differences between GPUs and CPUs, and delve into the software ecosystem enabling developers to harness GPU computing for data science. Finally, you will learn considerations for deploying AI workloads across different infrastructures, from on-premises data centers to models and multi-cloud setups.

What's included

7 videos5 assignments

In this module, we will visit infrastructure level considerations when deploying AI clusters. You will learn about requirements for multi-system AI clusters, such as the capabilities of NVIDIA GPUs and CPUs to address the requirements of AI workloads, storage, and networking considerations. We will discuss how energy efficient computing practices help data centers lower their carbon footprint, and how recommended design documents, or Reference Architectures (RAs), can be used as a foundation for building best-of-breed optimized AI systems. We will end this module discussing how cloud computing enhances AI deployments, outlining the key considerations for deploying AI in the cloud.

What's included

14 videos13 assignments

This last module covers key aspects involved in infrastructure management, monitoring, cluster orchestration, and job scheduling. You will identify the general concepts about provisioning, managing, and monitoring AI infrastructure, and describe the value and tools for cluster management. Finally, you will learn the key differences and common tools used for orchestration and scheduling, and the value of MLOps tools for continuous delivery and automation of AI workloads.

What's included

2 videos2 assignments

It is highly recommended that you complete all the course activities before you begin the quiz. Good luck!

What's included

1 reading1 assignment

Instructor

Instructor ratings
4.5 (23 ratings)
NVIDIA Training
NVIDIA
3 Courses28,337 learners

Offered by

NVIDIA

Recommended if you're interested in Machine Learning

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