Over 70 recipes to effectively apply DevOps best practices and implement Agile, Git, CI-CD & Test automation using Azure DevOps Server (TFS) 2019 Key Features Learn improving code quality using pull requests, branch policies, githooks and git branching design Accelerate the deployment of high quality software by automating build and releases using CI-CD Pipelines. Learn tried and tested techniques to automate database deployments, App Service & Function Deployments in Azure. Book DescriptionAzure DevOps Server, previously known as Team Foundation Server (TFS), is a comprehensive on-premise DevOps toolset with a rich ecosystem of open source plugins. This book is your one stop guide to learn how to effectively use all of these Azure DevOps services to go from zero to DevOps. You will start by building high-quality scalable software targeting .NET, .NET core or Node.js applications. You will learn techniques that will help you to set up end-to-end traceability of your code changes from design through to release. Whether you are deploying software on-premise or in the cloud in App Service, Functions, or Azure VMs, this book will help you learn release management techniques to reduce release failures. Next, you will be able to secure application configuration by using Azure KeyVault. You will also learn how to create and release extensions to the Azure DevOps marketplace and reach million developer ecosystem for feedback. The working extension samples will allow you to iterate changes in your extensions easily and release updates to the marketplace quickly. By the end of this book, techniques provided in the book will help you break down the invisible silos between your software development teams. This will transform you from being a good software development team to an elite modern cross functional software development team. What you will learn Set up a team project for an Agile delivery team, importing requirements from Excel Plan,track, and monitor progress using self updating boards, Sprint and Kanban boards Unlock the features of Git by using branch policies, Git pull requests, forks, and Git hooks Build and release .NET core, SQL and Node.js applications using Azure Pipeline Automate testing by integrating Microsoft and open source testing frameworks Extend Azure DevOps Server to a million developer ecosystem Who this book is forThis book is for anyone looking to succeed with DevOps. The techniques in this book apply to all roles of the software development lifecycle including developers, testers, architects, configuration analysts, site reliability engineers and release managers. If you are a new user you'll learn how to get started; if you are an experienced user you'll learn how to launch your project into a modern and mature DevOps enabled software development team.

Prepare to achieve AWS Machine Learning Specialty certification with this complete, up-to-date guide and take the exam with confidence Key Features Get to grips with core machine learning algorithms along with AWS implementation Build model training and inference pipelines and deploy machine learning models to the Amazon Web Services (AWS) cloud Learn all about the AWS services available for machine learning in order to pass the MLS-C01 exam Book DescriptionThe AWS Certified Machine Learning Specialty exam tests your competency to perform machine learning (ML) on AWS infrastructure. This book covers the entire exam syllabus using practical examples to help you with your real-world machine learning projects on AWS. Starting with an introduction to machine learning on AWS, you'll learn the fundamentals of machine learning and explore important AWS services for artificial intelligence (AI). You'll then see how to prepare data for machine learning and discover a wide variety of techniques for data manipulation and transformation for different types of variables. The book also shows you how to handle missing data and outliers and takes you through various machine learning tasks such as classification, regression, clustering, forecasting, anomaly detection, text mining, and image processing, along with the specific ML algorithms you need to know to pass the exam. Finally, you'll explore model evaluation, optimization, and deployment and get to grips with deploying models in a production environment and monitoring them. By the end of this book, you'll have gained knowledge of the key challenges in machine learning and the solutions that AWS has released for each of them, along with the tools, methods, and techniques commonly used in each domain of AWS ML. What you will learn Understand all four domains covered in the exam, along with types of questions, exam duration, and scoring Become well-versed with machine learning terminologies, methodologies, frameworks, and the different AWS services for machine learning Get to grips with data preparation and using AWS services for batch and real-time data processing Explore the built-in machine learning algorithms in AWS and build and deploy your own models Evaluate machine learning models and tune hyperparameters Deploy machine learning models with the AWS infrastructure Who this book is forThis AWS book is for professionals and students who want to prepare for and pass the AWS Certified Machine Learning Specialty exam or gain deeper knowledge of machine learning with a special focus on AWS. Beginner-level knowledge of machine learning and AWS services is necessary before getting started with this book.

Apply cloud design patterns to overcome real-world challenges by building scalable, secure, highly available, and cost-effective solutions Key Features Apply AWS Well-Architected Framework concepts to common real-world use cases Understand how to select AWS patterns and architectures that are best suited to your needs Ensure the security and stability of a solution without impacting cost or performance Book DescriptionOne of the most popular cloud platforms in the world, Amazon Web Services (AWS) offers hundreds of services with thousands of features to help you build scalable cloud solutions; however, it can be overwhelming to navigate the vast number of services and decide which ones best suit your requirements. Whether you are an application architect, enterprise architect, developer, or operations engineer, this book will take you through AWS architectural patterns and guide you in selecting the most appropriate services for your projects. AWS for Solutions Architects is a comprehensive guide that covers the essential concepts that you need to know for designing well-architected AWS solutions that solve the challenges organizations face daily. You'll get to grips with AWS architectural principles and patterns by implementing best practices and recommended techniques for real-world use cases. The book will show you how to enhance operational efficiency, security, reliability, performance, and cost-effectiveness using real-world examples. By the end of this AWS book, you'll have gained a clear understanding of how to design AWS architectures using the most appropriate services to meet your organization's technological and business requirements. What you will learn Rationalize the selection of AWS as the right cloud provider for your organization Choose the most appropriate service from AWS for a particular use case or project Implement change and operations management Find out the right resource type and size to balance performance and efficiency Discover how to mitigate risk and enforce security, authentication, and authorization Identify common business scenarios and select the right reference architectures for them Who this book is forThis book is for application and enterprise architects, developers, and operations engineers who want to become well-versed with AWS architectural patterns, best practices, and advanced techniques to build scalable, secure, highly available, and cost-effective solutions in the cloud. Although existing AWS users will find this book most useful, it will also help potential users understand how leveraging AWS can benefit their organization.

Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker's capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key Features Build, train, and deploy machine learning models quickly using Amazon SageMaker Analyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniques Improve productivity by training and fine-tuning machine learning models in production Book DescriptionAmazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker. You'll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you'll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You'll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you'll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy. By the end of this Amazon book, you'll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation. What you will learn Create and automate end-to-end machine learning workflows on Amazon Web Services (AWS) Become well-versed with data annotation and preparation techniques Use AutoML features to build and train machine learning models with AutoPilot Create models using built-in algorithms and frameworks and your own code Train computer vision and NLP models using real-world examples Cover training techniques for scaling, model optimization, model debugging, and cost optimization Automate deployment tasks in a variety of configurations using SDK and several automation tools Who this book is forThis book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.