This book presents an introductory perspective on how machine learning (ML) plays an important role in the VMware environment. It offers a basic understanding of how to leverage ML primitives, along with a deeper look into integration with VMware tools that are used for automation purposes today.
This book is intended for those planning, designing, and implementing the virtualization/cloud components of the Software-Defined Data Center foundational infrastructure. It helps users to put intelligence in their automation tasks to get self driving data center. It is assumed that the reader has knowledge of, and some familiarity with, virtualization concepts and related topics, including storage, security, and networking.
Chapter 1, Machine Learning Capabilities with vSphere 6.7, covers performance benchmarking on ML-based applications using GPUs in vSphere environment to support different customer business objectives.
Chapter 2, Proactive Measures with vSAN Advanced Analytics, explains how to improve the support experience for HCI environments, which will help customers maintain performance by rapidly resolving issues and minimizing downtime by means of proactive telemetry capabilities from vSAN Support Insight advanced analytics.
Chapter 3, Security with Workspace ONE Intelligence, describes an innovative approach to enterprise security for employees, apps, endpoints, and networks with access management, device, and app management, and for trusted analytics frameworks.
Chapter 4, Proactive Operations with VMware vRealize Suite, explains how to automate data centers and public clouds running on vSphere by injecting advanced analytics into its VMware vRealize Suite.
Chapter 5, Intent-Based Manifest with AppDefense, explains how to learn to use ML to create an intent-based manifest for an app running in a VM so as to secure the app against malicious behavior with an algorithm, which measures the run state against the intended state.
Chapter 6, ML-based Intelligent Log Management, covers how to innovative indexing and ML-based intelligent grouping in order to facilitate high-performance searching for faster troubleshooting across physical, virtual, and cloud environments by aiding fast troubleshooting through root cause analysis.
Chapter 7, Machine Learning as a Service in the Cloud, explains how to build and maintain each ML process with customization of the hardware and software and eliminate this complexity by automating the deployment of hardware resources, configuring them with the required operating system and application stack, and making them available to data scientists.
Chapter 8, ML-Based Rule Engine with Skyline, describes how to collect information from a customer and use ML as an intelligent rule engine to monitor whether anything deviates beyond normal behavior and then raise a red flag to offer proactive support.
Chapter 9, DevOps with vRealize Code Stream, looks into the highest priority processes to transform and apply techniques to compare and contrast the key differences between legacy operating models, processes, and team structures with the strategic operating model required for DevOps.
Chapter 10, Transforming VMware IT Operations Using ML, covers the operational challenges facing IT teams in this changing environment, and how they are resolving them to meet customer demands with the agility and scalability necessary to support rapid business innovation and growth.
Chatpter 11, Network Transformation with IoT, describes how to deliver data applications across regional boundaries, from heart monitors in hospitals to connected cars in cities, and wind turbines in rural regions, by embedding security into the architecture and managing data distribution from the data center to the cloud to the edge.
Chapter 12, Virtualizing Big Data on vSphere, explains how to leverage shared storage in modern big data platforms by evaluating first current in-memory big data platforms and how this fits in with virtualization with in-memory features of these platforms.
Chapter 13, Cloud Application Scaling, describes how to support cloud app development by providing developers access to traditional, cloud-native, and modern application development frameworks and resources, including container services and open APIs on a common virtualized environment.
Chapter 14, High-Performance Computing, goes into the learning capabilities provided by VMware vSphere to improve scientific productivity through features such as SR-IOV, RDMA, and vGPU to architect and meet the requirements for research computing, academic, scientific, and engineering workloads.
It is important to focus on identifying the benefits, complexities, risks, and related costs associated with building a complete automation engine, from the data center to the end user device, along with the applications.
To achieve this we will cover a diverse range of topics, beginning with advanced GPU configuration and their performance comparison to HPC and big data workloads running on the ever reliable vSphere platform which will help you to get the most out of this book.
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