Introduction to AI in the Data Center from Coursera

Welcome to the Introduction to AI in the Data Center Course!

As you know, Artificial Intelligence, or AI, is transforming society in many ways.

From speech recognition to improved supply chain management, AI technology provides enterprises with the compute power, tools, and algorithms their teams need to do their life’s work.

But how does AI work in a Data Center? What hardware and software infrastructure are needed?

These are some of the questions that this course will help you address.

This course will cover an introduction to concepts and terminology that will help you start the journey to AI and GPU computing in the data center.

You will learn about:

* AI and AI use cases, Machine Learning, Deep Learning, and how training and inference happen in a Deep Learning Workflow.

* The history and architecture of GPUs, how they differ from CPUs, and how they are revolutionizing AI.

* Deep learning frameworks, AI software stack, and considerations when deploying AI workloads on a data center on prem, in the cloud, on a hybrid model, or on a multi-cloud environment. 

* Requirements for multi-system AI clusters, considerations for infrastructure planning, including servers, networking, and storage and tools for cluster management, monitoring and orchestration.

This course is part of the preparation material for the NVIDIA Certified Associate - ”AI in the Data Center” certification.

This certification will take your expertise to the next level and support your professional development.

Who should take this course?

* IT Professionals

* System and Network Administrators

* DevOps

* Data Center Professionals

No prior experience required.

This is an introduction course to AI and GPU computing in the data center.

To learn more about NVIDIA’s certification program, visit:

https://academy.nvidia.com/en/nvidia-certified-associate-data-center/

So let's get started!

What's inside

Syllabus

Introduction to GPU Computing | NVIDIA Training

In this module you will see AI use cases in different industries, the concepts of AI, Machine Learning (ML) and Deep Learning (DL), understand what a GPU is, the differences between a GPU and a CPU. You will learn about the software ecosystem that has allowed developers to make use of GPU computing for data science and considerations when deploying AI workloads on a data center on prem, in the cloud, on a hybrid model, or on a multi-cloud environment.

Rack Level Considerations | NVIDIA Training

In this module we will cover rack level considerations when deploying AI clusters. You will learn about requirements for multi-system AI clusters, storage and networking considerations for such deployments, and an overview of NVIDIA reference architectures, which provide best practices to design systems for AI workloads.

Data Center Level Considerations | NVIDIA Training

This unit covers  data center level considerations  when deploying AI clusters, such as infrastructure provisioning and workload management, orchestration and job scheduling, tools for cluster management and monitoring, and power and cooling considerations for data center deployments.  Lastly, you will learn about AI infrastructure offered by NVIDIA partners through the DGX-ready data center colocation program.

Course Completion Quiz - Introduction to AI in the Data Center

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

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds a strong foundation for beginners who want to learn about AI in the data center

Introduces the history and architecture of GPUs, their differences from CPUs, and their revolutionary role in AI

Covers deep learning frameworks, AI software stack, and considerations for deploying AI workloads on a data center

Explores requirements for multi-system AI clusters, considerations for infrastructure planning, and tools for cluster management, monitoring, and orchestration

Provides hands-on experience through activities and a final quiz

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to AI in the Data Center with these activities:

Review Linear Algebra

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Ensure a solid foundation in linear algebra to enhance your comprehension of AI algorithms and techniques.

Browse courses on Linear Algebra

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Review the basics of linear algebra, including vectors, matrices, and transformations.
Practice solving linear equations and systems.
Apply your knowledge to simple AI algorithms, such as linear regression.

Python Programming

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Strengthen your Python programming skills to effectively implement AI algorithms and models.

Browse courses on Python Programming

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Review basic Python syntax and data structures.
Practice writing simple Python programs.
Work on a small Python project related to AI.

Review Introduction to AI

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Review the fundamental concepts of AI, machine learning, and deep learning to strengthen your understanding of the course material.

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Read the first three chapters of the book.
Summarize the key concepts of each chapter.
Identify the connections between the concepts and the course material.

Five other activities

Expand to see all activities and additional details

Show all eight activities

AI Notes Compilation

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Organize and expand your course materials by creating a comprehensive set of AI notes.

Browse courses on AI

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Review lecture notes, readings, and assignments.
Summarize key concepts and definitions.
Add additional notes and examples to enhance your understanding.

AI Study Group

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Deepen your understanding and help others by forming a study group where you discuss and work on AI projects together.

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Find a group of peers with similar interests.
Set regular meeting times.
Discuss course material, share resources, and work on projects together.

NVIDIA Deep Learning Institute Fundamentals

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Enhance your understanding of deep learning concepts and techniques by following interactive tutorials provided by NVIDIA.

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Complete the 'Introduction to Deep Learning' course.
Work through the 'Convolutional Neural Networks' tutorial.
Apply the learned concepts to a small project.

Kaggle AI Competitions

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Gain hands-on experience and test your AI skills by participating in Kaggle competitions.

Browse courses on Python Programming

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Choose a beginner-friendly competition.
Download the dataset and explore the data.
Build and train a model.
Submit your results and analyze your performance.

AI Blog

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Solidify your understanding of AI concepts by creating a blog that explains them to others.

Browse courses on Technical Writing

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Choose a specific AI topic to focus on.
Research the topic thoroughly.
Write a clear and concise explanation of the topic.
Publish your blog and share it with others.

Career center

Learners who complete Introduction to AI in the Data Center will develop knowledge and skills that may be useful to these careers:

Data Center Manager

A Data Center Manager manages and operates a data center. They are responsible for the physical infrastructure, power, cooling, and security of the data center. This course in AI and GPU computing in the data center is highly relevant as it provides essential knowledge about the design and operation of modern data centers, focusing on the latest AI technologies.

See salaries and explore the career path for Data Center Manager

AI Engineer

An AI Engineer builds and maintains artificial intelligence solutions. They use their understanding of computer science, math, and logic to design and implement AI algorithms into software applications. This course helps build a foundation in the important concepts of AI, Machine Learning, and Deep Learning, and how these are used together to create AI applications for a variety of use cases.

See salaries and explore the career path for AI Engineer

Machine Learning Engineer

A Machine Learning Engineer develops and maintains machine learning models. They use their understanding of statistics, computer science, and machine learning to design, implement, and evaluate machine learning solutions. This course introduces key concepts in AI and GPU computing used in machine learning engineering.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist collects, analyzes, and interprets data to extract meaningful insights. They use their knowledge of statistics, modeling, and data analysis to help businesses make data-driven decisions. This course in AI and GPU computing in the data center helps build a foundation for the data science field, enriching a Data Scientist's ability to apply new methods to process and analyze big data.

See salaries and explore the career path for Data Scientist

AI Researcher

An AI Researcher focuses on theoretical and practical advancing the field of artificial intelligence. They may work in academia or in the private sector, creating new ways to make machines perform tasks that usually require human intelligence. This course provides a solid foundation for understanding the key concepts, history, and tools involved in developing and implementing AI solutions.

See salaries and explore the career path for AI Researcher

Data Architect

A Data Architect designs and builds data architectures. They use their knowledge of data management, data modeling, and data integration to create data solutions that meet the needs of businesses. This course in AI and GPU computing in the data center can help Data Architects understand the latest trends and technologies in data management, and how they can be applied to AI and data science use cases.

See salaries and explore the career path for Data Architect

Cloud Architect

A Cloud Architect designs and builds cloud-based applications and services. They use their knowledge of cloud computing platforms, services, and tools to create scalable, resilient, and secure cloud solutions. This course in AI and GPU computing in the data center can be a great complement to this role, as it covers the essentials of infrastructure planning and deploying AI workloads on a data center, on prem, in the cloud, on a hybrid model, or on a multi-cloud environment.

See salaries and explore the career path for Cloud Architect

Systems Administrator

A Systems Administrator manages and maintains computer systems, networks, and data. They use their knowledge of computer hardware, software, and networking to ensure that systems are running smoothly and securely. This course in AI and GPU computing in the data center can be helpful for a Systems Administrator, as it gives a solid overview of the infrastructure components necessary for deploying AI applications in various environments.

See salaries and explore the career path for Systems Administrator

Network Administrator

A Network Administrator manages and maintains computer networks. They use their knowledge of network hardware, software, and protocols to ensure that networks are running smoothly and securely. This course may be useful for a Network Administrator, as it provides an introduction to AI and GPU computing and how these technologies are integrated into today's networks.

See salaries and explore the career path for Network Administrator

Project Manager

A Project Manager plans, executes, and closes projects. They use their knowledge of project management tools and techniques to ensure that projects are completed on time, within budget, and to the required quality. This course may be useful for a Project Manager as it provides an introduction to AI and GPU computing, and how these technologies can be used to improve project planning and execution.

See salaries and explore the career path for Project Manager

Business Intelligence Analyst

A Business Intelligence Analyst collects, analyzes, and interprets data to provide insights to businesses. They use their knowledge of business intelligence tools and techniques to help businesses make better decisions. This course may be useful for a Business Intelligence Analyst as it provides an introduction to AI and GPU computing, and how these technologies can be used to improve data analysis and decision-making.

See salaries and explore the career path for Business Intelligence Analyst

Software Developer

A Software Developer designs, develops, and maintains software applications. They use their knowledge of programming languages, software development tools, and software design patterns to create software solutions that meet the needs of users. This course may be useful for a Software Developer as it provides an introduction to AI and GPU computing, and how these technologies can be used to improve software development and performance.

See salaries and explore the career path for Software Developer

DevOps Engineer

A DevOps Engineer creates tools and processes to improve communication and coordination between development and operations teams. They use their knowledge of software development, IT operations, and automation to improve the efficiency and reliability of software delivery. This course may be useful for a DevOps Engineer as it provides an introduction to AI and GPU computing.

See salaries and explore the career path for DevOps Engineer

Product Manager

A Product Manager defines and manages the development of products. They use their knowledge of product management tools and techniques to ensure that products meet the needs of users. This course may be useful for a Product Manager as it provides an introduction to AI and GPU computing, and how these technologies can be used to improve product development and delivery.

See salaries and explore the career path for Product Manager

Consultant

A Consultant provides advice and guidance to clients on a variety of topics. They use their knowledge of business, technology, and management to help clients solve problems and achieve their goals. This course may be useful for a Consultant as it provides an introduction to AI and GPU computing, and how these technologies can be used to improve consulting services.

See salaries and explore the career path for Consultant