Bøker av Navin Sabharwal

Welcome 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 algorithmsUse algorithms to implement AIOps use casesUse 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 baseliningUse anomaly detection techniques in AIOps Who This Book is ForAIOps 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

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 LearnUnderstand how cloud computing and microservices architecture are changing operations dynamicsUnderstand ITIL, IT4IT, and LeanLearn how Site Reliability Engineering, Agile and DevOps work in tandemLeverage Agile, Scrum, Kanban, Scrumban, and Spotify models to run cloud operationsUse Site Reliability techniques along with Agile and DevOpsStudy 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 Who This Book is ForInfrastructure architects, DevOps architects,  Agile practitioners, DevSecOps Experts, Product Managers/Scrum Masters, DevOps Engineers.

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 Examine the fundamentals of word embeddings Apply neural networks and BERT for various NLP tasks Develop a question-answering system from scratch Train question-answering systems for your own data Who This Book Is ForAI and machine learning developers and natural language processing developers.

Discover the methodologies and best practices for getting started with Google cloud automation services including Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins to automate deployment of cloud infrastructure and applications. The book begins with an introduction to Google cloud services and takes you through the various platforms available to do automation on the GCP platform. You will do hands-on exercises and see best practices for using Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins. You'll cover the automation aspects of the Google Cloud Platform holistically using native and upcoming open source technologies. The authors cover the entire spectrum of automation from cloud infrastructure to application deployment and tie everything together in a release pipeline using Jenkins. Pro Google Cloud Automation provides in-depth guidance on automation and deployment of microservices-based applications running on the Kubernetes platform. It provides sample code and best practice guidance for developers and architects for their automation projects on the Google Cloud Platform. This book is a good starting point for developers, architects, and administrators who want to learn about Google cloud automation. What You Will Learn Gain the fundamentals of Google's automation-enabling servicesSee an architecture overview for Google Cloud Deployment Manager, Spinnaker, Tekton, and JenkinsImplement automation for infrastructure and application use casesAutomate microservices-based applications running on GKEEnable Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins Who This Book Is For Developers, architects, and administrators who want to learn about Google cloud automation.

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.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 "e;gcloud"e; command line interface.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.What You Will LearnUnderstand the main container services in GCP (Google Container Registry, Google Kubernetes Engine, Kubernetes Engine, Management Services)Perform hands-on steps to deploy, secure, scale, monitor, and automate your containerized environmentDeploy a sample microservices application on GKEDeploy monitoring for your GKE environmentUse DevOps automation in the CI/CD pipeline and integrate it with GKEWho This Book Is ForArchitects, developers, and DevOps engineers who want to learn Google Kubernetes Engine

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.