Cloud Capacity Management helps readers in understanding what the cloud, IaaS, PaaS, SaaS are, how they relate to capacity planning and management and which stakeholders are involved in delivering value in the cloud value chain. It explains the role of capacity management for a creator, aggregator, and consumer of cloud services and how to provision for it in a 'pay as you use model'.
 This involves a high level of abstraction and virtualization to facilitate rapid and on demand provisioning of services. The conventional IT service models take a traditional approach when planning for service capacity to provide optimum services levels which has huge cost implications for service providers. 
 This book addresses the gap areas between traditional capacity management practices and cloud service models. It also showcases capacity management process design and implementation in a cloud computing domain using ITSM best practices. This book is a blend of ITSM best practices and infrastructure capacity planning and optimization implementation in various cloud scenarios. 
 Cloud Capacity Management addresses the basics of cloud computing, its various models, and their impact on capacity planning. This book also highlights the infrastructure capacity management implementation process in a cloud environment showcasing inherent capabilities of tool sets available and the various techniques for capacity planning and performance management. Techniques like dynamic resource scheduling, scaling, load balancing, and clustering etc are explained for implementing capacity management.

Cloud Capacity Management: Capacity Management (Expert's Voice in Information Technology)

Computers & Technology

Sabharwal, Navin

Networking & Cloud Computing

Apress

Discover the methodologies and best practices for getting started with Google Cloud Platform relational services – CloudSQL and CloudSpanner. The book begins with the basics of working with the Google Cloud Platform along with an introduction to the database technologies available for developers from Google Cloud. You'll then take an in-depth hands on journey into Google CloudSQL and CloudSpanner, including choosing the right platform for your application needs, planning, provisioning, designing and developing your application. Sample applications are given that use Python to connect to CloudSQL and CloudSpanner, along with helpful features provided by the engines. You''ll also implement practical best practices in the last chapter. Hands On Google Cloud SQL and Cloud Spanner is a great starting point to apply GCP data offerings in your technology stack and the code used allows you to try out the examples and extend them in interesting ways. What You'll Learn
<ul>
<li>Get started with Big Data technologies on the Google Cloud Platform </li>
<li>Review CloudSQL and Cloud Spanner from basics to administration</li>
<li>Apply best practices and use Google’s CloudSQL and CloudSpanner offering</li>
<li>Work with code in Python notebooks and scripts</li>
</ul>
Who This Book Is For Application architects, database architects, software developers, data engineers, cloud architects.

Hands On Google Cloud SQL and Cloud Spanner: Deployment, Administration and Use Cases with Python

Computer Science

Follow a step-by-step, hands-on approach to building production-ready enterprise cognitive virtual assistants using Google Dialogflow. This book provides an overview of the various cognitive technology choices available and takes a deep dive into cognitive virtual agents for handling complex real-life use cases in various industries such as travel and weather. You'll delve deeper into the advanced features of cognitive virtual assistants implementing features such as input/output context, follow-up intents, actions and parameters, and handling complex multiple intents. You'll learn how to integrate with third-party messaging platforms by integrating your cognitive bot with Facebook messenger. You'll also integrate with third-party APIs to enrich your cognitive bots using webhooks. Cognitive Virtual Assistants Using Google Dialogflow takes the complexity out of the cognitive platform and provides rich guidance which you can use when developing your own cognitive bots. The book covers Google Dialogflow in-depth and starts with the basics, serving as a hands-on guide for developers who are starting out on their journey with Google Dialogflow. All the code presented in the book will be available in the form of scripts and configuration files, which allows you to try out the examples and extend them in interesting ways. What You Will Learn <ul> <li>Develop cognitive bots with Google Dialogflow technology</li> <li>Use advanced features to handle complex conversation scenarios</li> <li>Enrich the bot's conversations by understanding the sentiment of the user</li> <li>See best practices for developing cognitive bots</li> <li>Enhance a cognitive bot by integrating with third-party services</li> </ul> Who This Book Is For AI and ML developers.

Cognitive Virtual Assistants Using Google Dialogflow: Develop Complex Cognitive Bots Using the Google Dialogflow Platform

Agrawal, Amit

Discover the methodologies and best practices for getting started with container services monitoring using Prometheus, AppDynamics, and Dynatrace.&nbsp;The book begins with the basics of working with the containerization and microservices architecture while establishing the need for monitoring and management technologies. You'll go through hands-on deployment, configuration, and best practices for Prometheus. Next, you'll delve deeper into monitoring of container ecosystems for availability, performance, and logs, and then cover the reporting capabilities of Prometheus. Further, you'll move on to advanced topics of extending Prometheus including how to develop new use cases and scenarios.&nbsp; You'll then use enterprise tools such as AppDynamics and Wavefront to discover deeper application monitoring best practices. You'll conclude with fully automated deployment of the monitoring and management platforms integrated with the container ecosystem using infrastructure-as -code tools such as Jenkins, Ansible and Terraform. The book provides sample code and best practices for you to look at container monitoring from a holistic viewpoint.&nbsp; This book is a good starting point for developers, architects, and administrators who want to learn about monitoring and management of cloud native and microservices containerized applications.&nbsp;&nbsp; What You Will Learn <ul> <li>Examine the fundamentals of container monitoring</li> <li>Get an overview of the architecture for Prometheus and Alert Manager</li> <li>Enable Prometheus monitoring for containers</li> <li>Monitor containers using Wavefront</li> <li>Use the guidelines on container monitoring with enterprise solutions AppDynamics and Wavefront</li> </ul> Who This Book Is For&nbsp;Software developers,&nbsp;system administrators, and DevOps engineers working for enterprise customers who want to use monitoring solutions for their container ecosystems.&nbsp;

Monitoring Microservices and Containerized Applications: Deployment, Configuration, and Best Practices for Prometheus and Alert Manager

Pandey, Piyush

Discover methodologies and best practices for getting started with Google Kubernetes Engine (GKE). This book helps you understand how GKE provides a fully managed environment to deploy and operate containerized applications on Google Cloud infrastructure. <div> </div> <div>You will see how Kubernetes makes it easier for users to manage clusters and the container ecosystem. And you will get detailed guidance on deploying and managing applications, handling administration of container clusters, managing policies, and monitoring cluster resources. You will learn how to operate the GKE environment through the GUI-based Google Cloud console and the "gcloud" command line interface.</div> <div> The book starts with an introduction to GKE and associated services. The authors provide hands-on examples to set up Container Registry and GKE Cluster, and you will follow through an application deployment on GKE. Later chapters focus on securing your GCP GKE environment, GKE monitoring and dashboarding, and CI/CD automation. All of the code presented in the book is provided in the form of scripts, which allow you to try out the examples and extend them in interesting ways. <div>What You Will Learn</div> <div> <ul> <li>Understand the main container services in GCP (Google Container Registry, Google Kubernetes Engine, Kubernetes Engine, Management Services) </li> <li>Perform hands-on steps to deploy, secure, scale, monitor, and automate your containerized environment</li> <li>Deploy a sample microservices application on GKE</li> <li>Deploy monitoring for your GKE environment</li> <li>Use DevOps automation in the CI/CD pipeline and integrate it with GKE</li> </ul> </div> <div> </div> <div>Who This Book Is For</div> <div> </div> <div>Architects, developers, and DevOps engineers who want to learn Google Kubernetes Engine </div> </div>

Pro Google Kubernetes Engine: Network, Security, Monitoring, and Automation Configuration

<div> Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning. The book begins with an overview of the technology landscape behind BERT. It takes you through the basics of NLP, including natural language understanding with tokenization, stemming, and lemmatization, and bag of words. Next, you'll look at neural networks for NLP starting with its variants such as recurrent neural networks, encoders and decoders, bi-directional encoders and decoders, and transformer models. Along the way, you'll cover word embedding and their types along with the basics of BERT. After this solid foundation, you'll be ready to take a deep dive into BERT algorithms such as masked language models and next sentence prediction. You'll see different BERT variations followed by a hands-on example of a question answering system. Hands-on Question Answering Systems with BERT is a good starting point for developers and data scientists who want to develop and design NLP systems using BERT. It provides step-by-step guidance for using BERT. What You Will Learn <ul> <li>Examine the fundamentals of word embeddings</li> <li>Apply neural networks and BERT for various NLP tasks</li> <li style="list-style: none">Develop a question-answering system from scratch</li> <li>Train question-answering systems for your own data</li> </ul> Who This Book Is For AI and machine learning developers and natural language processing developers. </div>

Hands-on Question Answering Systems with BERT: Applications in Neural Networks and Natural Language Processing

Discover the methodologies and best practices for getting started with HashiCorp tools, including Terraform, Vault, and Packer. The book begins with an introduction to the infrastructure-as-code concept while establishing the need for automation and management technologies. You'll go over hands-on deployment, configuration, and best practices for Terraform, Packer, Vault, Nomad, and Consul. You'll then delve deeper into developing automation code using Terraform for automating AWS/Azure/GCP public cloud tasks; advanced topics include leveraging Vault for secrets management and Packer for image management. Along the way you will also look at Nomad and Consul for managing application orchestration along with network interconnectivity. In each chapter you will cover automated infrastructure and application deployment on the VM/container base ecosystem. The book provides sample code and best-practice guidance for developers and architects to look at infrastructure-as-code adoption from a holistic viewpoint.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; All the code presented in the book is available in the form of scripts, which allow you to try out the examples and extend them in interesting ways.&nbsp; What You Will Learn <ul> <li>Get an overview of the architecture of Terraform, Vault, Packer, Nomad, and Consul</li> <li>Follow hands-on steps for enabling Terraform, Vault, Packer, Nomad, and Consul</li> <li>Automate various services on the public cloud, including AWS, Azure, and GCP&nbsp;</li> </ul> Who This Book Is For&nbsp; Developers, architects, and administrators who want to learn about infrastructure-as-code automation.

Infrastructure-as-Code Automation Using Terraform, Packer, Vault, Nomad and Consul: Hands-on Deployment, Configuration, and Best Practices

Pandey, Sarvesh

Discover the best practices for transforming cloud and infrastructure operations by using Agile, Scrum, Kanban, Scrumban and Spotify models. This book will help you gain an in-depth understanding of these processes so that you can apply them to your own work. 
The book begins by offering an overview of current processes and methods used in IT Operations using ITIL and IT4IT. The Authors provide a background of the Agile, Scrum, Kanban, SaFe, Scrumban, and Spotify models used in software development. You’ll then gain in-depth guidance and best practices to implement Agile in the Operations world. You’ll see how Agile, Site Reliability Engineering and DevOps work in tandem to provide the foundation for modern day infrastructure and cloud operations. The book also offers a comparison of various agile processes and their suitability to the infrastructure and cloud operations world. 
After completing this is hands-on guide, you’ll know how to adopt Agile, DevOps and SRE and select the most suitable processes for your organization to achieve higher reliability, agility and lower costs while running cloud and infrastructure operations.What You Will Learn\nUnderstand how cloud computing and microservices architecture are changing operations dynamics Understand ITIL, IT4IT, and Lean Learn how Site Reliability Engineering, Agile and DevOps work in tandem Leverage Agile, Scrum, Kanban, Scrumban, and Spotify models to run cloud operations Use Site Reliability techniques along with Agile and DevOps Study the different agile frameworks (Spotify, SAFe, LeSS, DAD, Nexus), their purpose, benefits and implementation approaches. Learn a step-by-step process to identify and implement these frameworks in your organization\nWho This Book is For 
Infrastructure architects, DevOps architects, Agile practitioners, DevSecOps Experts, Product Managers/Scrum Masters, DevOps Engineers.\nFrom the Back Cover\nDiscover the best practices for transforming cloud and infrastructure operations by using Agile, Scrum, Kanban, Scrumban and Spotify models. This book will help you gain an in-depth understanding of these processes so that you can apply them to your own work. 
The book begins by offering an overview of current processes and methods used in IT Operations using ITIL and IT4IT. The Authors provide a background of the Agile, Scrum, Kanban, SaFe, Scrumban, and Spotify models used in software development. You’ll then gain in-depth guidance and best practices to implement Agile in the Operations world. You’ll see how Agile, Site Reliability Engineering and DevOps work in tandem to provide the foundation for modern day infrastructure and cloud operations. The book also offers a comparison of various agile processes and their suitability to the infrastructure and cloud operations world. 
After completing this is hands-on guide, you’ll know how to adopt Agile, DevOps and SRE and select the most suitable processes for your organization to achieve higher reliability, agility and lower costs while running cloud and infrastructure operations. 
You will: 
Understand how cloud computing and microservices architecture are changing operations dynamics Understand ITIL, IT4IT, and Lean Learn how Site Reliability Engineering, Agile and DevOps work in tandem Leverage Agile, Scrum, Kanban, Scrumban, and Spotify models to run cloud operations Use Site Reliability techniques along with Agile and DevOps Study the different agile frameworks (Spotify, SAFe, LeSS, DAD, Nexus), their purpose, benefits and implementation approaches. Learn a step-by-step process to identify and implement these frameworks in your organization

Hands-On Guide to AgileOps: A Guide to Implementing Agile, DevOps, and SRE for Cloud Operations

Rathore, Raminder

Agrawal, Udita

From the Back Cover\nWelcome to your hands-on guide to artificial intelligence for IT operations (AIOps). This book provides in-depth coverage, including operations and technical aspects. The fundamentals of machine learning (ML) and artificial intelligence (AI) that form the core of AIOps are explained as well as the implementation of multiple AIOps uses cases using ML algorithms. 
The book begins with an overview of AIOps, covering its relevance and benefits in the current IT operations landscape. The authors discuss the evolution of AIOps, its architecture, technologies, AIOps challenges, and various practical use cases to efficiently implement AIOps and continuously improve it. The book provides detailed guidance on the role of AIOps in site reliability engineering (SRE) and DevOps models and explains how AIOps enables key SRE principles. 
The book provides ready-to-use best practices for implementing AIOps in an enterprise. Each component of AIOps and ML using Python code and templates is explained and shows how ML can be used to deliver AIOps use cases for IT operations. 
What You Will Learn 
Know what AIOps is and the technologies involved Understand AIOps relevance through use cases Understand AIOps enablement in SRE and DevOps Understand AI and ML technologies and algorithms Use algorithms to implement AIOps use cases 
Use best practices and processes to set up AIOps practices in an enterprise Know the fundamentals of ML and deep learning Study a hands-on use case on de-duplication in AIOps Use regression techniques for automated baselining Use anomaly detection techniques in AIOps\nWelcome to your hands-on guide to artificial intelligence for IT operations (AIOps). This book provides in-depth coverage, including operations and technical aspects. The fundamentals of machine learning (ML) and artificial intelligence (AI) that form the core of AIOps are explained as well as the implementation of multiple AIOps uses cases using ML algorithms. 
The book begins with an overview of AIOps, covering its relevance and benefits in the current IT operations landscape. The authors discuss the evolution of AIOps, its architecture, technologies, AIOps challenges, and various practical use cases to efficiently implement AIOps and continuously improve it. The book provides detailed guidance on the role of AIOps in site reliability engineering (SRE) and DevOps models and explains how AIOps enables key SRE principles. 
The book provides ready-to-use best practices for implementing AIOps in an enterprise. Each component of AIOps and ML using Python code and templates is explained and shows how ML can be used to deliver AIOps use cases for IT operations. 
What You Will Learn 
Know what AIOps is and the technologies involved Understand AIOps relevance through use cases Understand AIOps enablement in SRE and DevOps Understand AI and ML technologies and algorithms Use algorithms to implement AIOps use cases 
Use best practices and processes to set up AIOps practices in an enterprise Know the fundamentals of ML and deep learning Study a hands-on use case on de-duplication in AIOps Use regression techniques for automated baselining Use anomaly detection techniques in AIOps 
Who This Book is For 
AIOps enthusiasts, monitoring and management consultants, observability engineers, site reliability engineers, infrastructure architects, cloud monitoring consultants, service management experts, DevOps architects, DevOps engineers, and DevSecOps experts

Hands-on AIOps: Best Practices Guide to Implementing AIOps

Bhardwaj, Gaurav

See the best price to sell, buy, or rent books by Sabharwal, Navin. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Sabharwal, Navin

See the best price to sell, buy, or rent books by Sabharwal, Navin. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Cloud Capacity Management: Capacity Management (Expert's Voice in Information Technology)

Hands On Google Cloud SQL and Cloud Spanner: Deployment, Administration and Use Cases with Python

Cognitive Virtual Assistants Using Google Dialogflow: Develop Complex Cognitive Bots Using the Google Dialogflow Platform

Monitoring Microservices and Containerized Applications: Deployment, Configuration, and Best Practices for Prometheus and Alert Manager

Pro Google Kubernetes Engine: Network, Security, Monitoring, and Automation Configuration

Hands-on Question Answering Systems with BERT: Applications in Neural Networks and Natural Language Processing

Hands-on Question Answering Systems with BERT: Applications in Neural Networks and Natural Language Processing

Infrastructure-as-Code Automation Using Terraform, Packer, Vault, Nomad and Consul: Hands-on Deployment, Configuration, and Best Practices

Hands-On Guide to AgileOps: A Guide to Implementing Agile, DevOps, and SRE for Cloud Operations

Hands-on AIOps: Best Practices Guide to Implementing AIOps