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 For AI and machine learning developers and natural language processing developers.

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 "gcloud" 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 Learn Understand 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 environment Deploy a sample microservices application on GKE Deploy monitoring for your GKE environment Use DevOps automation in the CI/CD pipeline and integrate it with GKE Who This Book Is For Architects, developers, and DevOps engineers who want to learn Google Kubernetes Engine

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 services See an architecture overview for Google Cloud Deployment Manager, Spinnaker, Tekton, and Jenkins Implement automation for infrastructure and application use cases Automate microservices-based applications running on GKE Enable 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 the methodologies and best practices for getting started with container services monitoring using Prometheus, AppDynamics, and Dynatrace. 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. 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. 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. What You Will Learn Examine the fundamentals of container monitoring Get an overview of the architecture for Prometheus and Alert Manager Enable Prometheus monitoring for containers Monitor containers using Wavefront Use the guidelines on container monitoring with enterprise solutions AppDynamics and Wavefront Who This Book Is For Software developers, system administrators, and DevOps engineers working for enterprise customers who want to use monitoring solutions for their container ecosystems.

Apply best practices for deploying and administering HCL Workload automation (HWA) to meet the automation requirements of the digitally transformed platform. This book will provide detailed architecture and deployment options to achieve this goal. Workload automation focuses on real-time processing, predefined event-driven triggers, and situational dependencies. It offers centralized control of managing multiple tasks, making it possible to schedule enterprise-wide tasks. You'll see how it supports the timely completion of tasks and is beneficial for processes that need to happen at a specific time or need to occur as a result of another event. HWA increases efficiency, reduces the turnaround time for workflows, and reduces errors along with delays in end-to-end processes. You'll review proven ways to deliver batch optimization and modernization requirements, and see how solutions can be aligned with the DevSecOps delivery model. Workload Automation Using HWA presents information on how to use the tool and has numerous use cases and implementation procedures to guide every workload automation deployment requirement. What You'll Learn Automate and integrate your complex workload, workflow, and business processes across automation platforms, ERP systems, and business applications Understand event-driven batch automation Practice alignment of the workload automation solution with the DevSecOps principles Who This Book Is For Solution Architects, Infrastructure Architects, Technical Architects, Enterprise Architects, Workload Automation Tool Administrators or SME's, Schedulers, Application owners, Automation Specialists, Service Delivery Managers

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 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

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 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 Who This Book is For Infrastructure architects, DevOps architects, Agile practitioners, DevSecOps Experts, Product Managers/Scrum Masters, DevOps Engineers.

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. 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. What You Will Learn Get an overview of the architecture of Terraform, Vault, Packer, Nomad, and Consul Follow hands-on steps for enabling Terraform, Vault, Packer, Nomad, and Consul Automate various services on the public cloud, including AWS, Azure, and GCP Who This Book Is For Developers, architects, and administrators who want to learn about infrastructure-as-code automation.

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 Get started with Big Data technologies on the Google Cloud Platform Review CloudSQL and Cloud Spanner from basics to administration Apply best practices and use Google's CloudSQL and CloudSpanner offering Work with code in Python notebooks and scripts Who This Book Is For Application architects, database architects, software developers, data engineers, cloud architects.

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.This book also covers emerging techniques in the cloud capacity management area: * Self learning systems * Yield management * Proactive capacity planning What you'll learn * Cloud computing and virtualization basics and models * Cloud service delivery models and service providers value chain explained in depth * A practical approach for capacity planning in cloud environments * Capacity management implementation procedures and guidelines specifically designed for cloud environments Who this book is for This book would be of help to technical consultants involved in virtualization, capacity managers, capacity analysts, cloud architects, ITIL consultants, practitioners, cloud developers and cloud consultants. Service level managers, technical managers, IT managers, process analyst and process consultants may also find this book helpful for guidance on the protocols involved.

Cognitive Virtual Bots are taking the technology and user experience world by storm. This book provides clear guidance on how different cognitive platforms can be used to develop Cognitive Virtual Assistants that enable a conversation by using DialogFlow and advanced Natural Language Processing. You will start by understanding the technology landscape and various use cases that Cognitive Virtual Assistants can be used in. Early chapters will take you through the basics of Cognitive Virtual Assistants, before moving onto advanced concepts and hands on examples of using IBM Watson Assistant and its advanced configurations with Watson Discovery Services, Watson Knowledge Studio and Spellchecker Service. You'll then examine integrations that enrich the Cognitive Virtual Assistant by providing data around weather, locations, stock markets. The book concludes by providing a glimpse of what to expect in the future for Cognitive Virtual Assistants. What You'll Learn Review the fundamentals of Cognitive Virtual Assistants. Develop a Cognitive Virtual Assistant from scratch using IBM Watson platform. Integrate and enrich your Virtual Agent with other services such as weather, location and stocks. Instantly deliver your bot on major messaging channels such as Skype, SMS, and Webchat Train your Cognitive Virtual Agent on specific use cases. Who This Book Is ForAI and machine learning engineers, cognitive solutions architects and developers would find the book extremely useful

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 Develop cognitive bots with Google Dialogflow technology Use advanced features to handle complex conversation scenarios Enrich the bot's conversations by understanding the sentiment of the user See best practices for developing cognitive bots Enhance a cognitive bot by integrating with third-party services Who This Book Is For AI and ML developers.

Automation through Chef Opscode provides an in-depth understanding of Chef, which is written in Ruby and Erlang for configuration management, cloud infrastructure management, system administration, and network management. Targeted at administrators, consultants, and architect, the book guides them through the advanced features of the tool which are necessary for infrastructure automation, devops automation, and reporting. The book presumes knowledge of Ruby and Erlang which are used as reference languages for creating recipes and cookbooks and as a refresher on them to help the reader get on speed with the flow of book. The book provides step by step instructions on installation and configuration of Chef, usage scenarios of Chef, in infrastructure automation by providing common scenarios like virtual machine provisioning, OS configuration for Windows, Linux, and Unix, provisioning and configuration of web servers like Apache along with popular databases like MySQL. It further elaborates on the creation of recipes, and cookbooks, which help in deployment of servers and applications to any physical, virtual, or cloud location, no matter the size of the infrastructure. The books covers advanced features like LWRPs and Knife and also contains several illustrative sample cookbooks on MySQL, Apache, and CouchDB deployment using a step by step approach.

There has been extreme hype about Cloud Computing from years and this word is in headlines of IT world news. Why not? Cloud computing has revolutionized the entire paradigm of computing and technology and has helped businesses in coming closer to technology. This book covers basics of Virtualization and Cloud Computing along and also includes various practices that is followed by Major Service Providers in the current market. Virtualization is the core engine of Cloud Computing and is the base of how the services are provided to the consumers. Cloud computing procedures and techniques for supporting business processes and applications. The details of various layers of Cloud Computing - IaaS, PaaS and SaaS. The book also describes the details and differences between various deployment models of Cloud, i.e. Private Cloud, Public Cloud, Hybird & Community Cloud. The book also covers the overview of how the major service providers for all the types of cloud services in the market use various technologies and provides efficient services to the customers. The book describes features and services offered by various service providers in all the models of cloud, Infrastructure as a Services, Platform as a Service & Software as a service. The major service providers covered in this book are Amazon Web Service, VMware VCloud Director, GoGrid & Rackspace in Infrastructure as a Service. Major service providers in Platform as a Service covered in this book are Microsoft Windows Azure, Heroku, VMware CloudFoundary, Google App Engine & Force.com. Major service providers in Platform as a Service covered in the book are Microsoft Office 365, SalesForce, Workday & Google Apps.