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Intelligent Automation with VMware

Intelligent Automation with VMware

By : Kundan
5 (2)
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Intelligent Automation with VMware

Intelligent Automation with VMware

5 (2)
By: Kundan

Overview of this book

This book presents an introductory perspective on how machine learning plays an important role in a VMware environment. It offers a basic understanding of how to leverage machine learning primitives, along with a deeper look into integration with the VMware tools used for automation today. This book begins by highlighting how VMware addresses business issues related to its workforce, customers, and partners with emerging technologies such as machine learning to create new, intelligence-driven, end user experiences. You will learn how to apply machine learning techniques incorporated in VMware solutions for data center operations. You will go through management toolsets with a focus on machine learning techniques. At the end of the book, you will learn how the new vSphere Scale-Out edition can be used to ensure that HPC, big data performance, and other requirements can be met (either through development or by fine-tuning guidelines) with mainstream products.
Table of Contents (19 chapters)
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Section 1: VMware Approach with ML Technology
7
Section 2: ML Use Cases with VMware Solutions
13
Section 3: Dealing with Big Data, HPC , IoT, and Coud Application Scalability through ML

Summary

Hadoop is still quite new for many enterprises, and different companies are at different stages in their Hadoop adoption journey. Having worked with several customers on this, it's evident that, depending on the stage the customer is at, there are distinct Hadoop use cases and requirements. Virtualization can help to address all the key requirements of each stages. Multiple Hadoop clusters can be used by different departments within a company.

It is difficult to manage multiple clusters for different departments in a company and keep them all running well. We have multiple use cases running data mining, recommendation engines, and for our online service, we have one shared dataset, rather than duplicated data everywhere. We are now managing a single cluster rather than multiple clusters. 

In the next chapter, we will learn how to support cloud-native application...

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