Azure Data Science Vm

  azure data science vm: Microsoft Designing and Implementing a Data Science Solution on Azure Exam Practice Questions & Dumps Exam Snap, The Azure Data Scientist applies their knowledge of data science and machine learning to implement and run machine learning workloads on Azure; in particular, using Azure Machine Learning Service. This entails planning and creating a suitable working environment for data science workloads on Azure, running data experiments and training predictive models, managing and optimizing models, and deploying machine learning models into production. Preparing For The Designing and Implementing a Data Science Solution on Azure DP-100 Exam To Become A Certified Designing and Implementing a Data Science Solution on Azure By Microsoft ? Here We Have Brought Best Exam Questions For You So That You Can Prepare Well For This Exam. Unlike other online simulation practice tests, you get an eBook version that is easy to read & remember these questions. You can simply rely on these questions for successfully certifying this exam.
  azure data science vm: Microsoft Azure Essentials Azure Machine Learning Jeff Barnes, 2015-04-25 Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services. Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the Microsoft Azure Essentials series.
  azure data science vm: Hands-On Machine Learning with Azure Thomas K Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak, 2018-10-31 Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies Key FeaturesLearn advanced concepts in Azure ML and the Cortana Intelligence Suite architectureExplore ML Server using SQL Server and HDInsight capabilitiesImplement various tools in Azure to build and deploy machine learning modelsBook Description Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure. By the end of this book, you will be fully equipped to implement smart cognitive actions in your models. What you will learnDiscover the benefits of leveraging the cloud for ML and AIUse Cognitive Services APIs to build intelligent botsBuild a model using canned algorithms from Microsoft and deploy it as a web serviceDeploy virtual machines in AI development scenariosApply R, Python, SQL Server, and Spark in AzureBuild and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlowImplement model retraining in IoT, Streaming, and Blockchain solutionsExplore best practices for integrating ML and AI functions with ADLA and logic appsWho this book is for If you are a data scientist or developer familiar with Azure ML and cognitive services and want to create smart models and make sense of data in the cloud, this book is for you. You’ll also find this book useful if you want to bring powerful machine learning services into your cloud applications. Some experience with data manipulation and processing, using languages like SQL, Python, and R, will aid in understanding the concepts covered in this book
  azure data science vm: Microsoft Azure Essentials - Fundamentals of Azure Michael Collier, Robin Shahan, 2015-01-29 Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. The first ebook in the series, Microsoft Azure Essentials: Fundamentals of Azure, introduces developers and IT professionals to the wide range of capabilities in Azure. The authors - both Microsoft MVPs in Azure - present both conceptual and how-to content for key areas, including: Azure Websites and Azure Cloud Services Azure Virtual Machines Azure Storage Azure Virtual Networks Databases Azure Active Directory Management tools Business scenarios Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the “Microsoft Azure Essentials” series.
  azure data science vm: Hands-on Cloud Analytics with Microsoft Azure Stack Prashila Naik, 2020-11-12 Explore and work with various Microsoft Azure services for real-time Data Analytics KEY FEATURESÊ Understanding what Azure can do with your data Understanding the analytics services offered by Azure Understand how data can be transformed to generate more data Understand what is done after a Machine Learning model is builtÊ Go through some Data Analytics real-world use cases ÊÊ DESCRIPTIONÊ Data is the key input for Analytics. Building and implementing data platforms such as Data Lakes, modern Data Marts, and Analytics at scale require the right cloud platform that Azure provides through its services. The book starts by sharing how analytics has evolved and continues to evolve. Following the introduction, you will deep dive into ingestion technologies. You will learn about Data processing services in Azure. You will next learn about what is meant by a Data Lake and understand how Azure Data Lake Storage is used for analytical workloads. You will then learn about critical services that will provide actual Machine Learning capabilities in Azure. The book also talks about Azure Data Catalog for cataloging, Azure AD for Access Management, Web Apps and PowerApps for cloud web applications, Cognitive services for Speech, Vision, Search and Language, Azure VM for computing and Data Science VMs, Functions as serverless computing, Kubernetes and Containers as deployment options. Towards the end, the book discusses two use cases on Analytics. WHAT WILL YOU LEARNÊÊ Explore and work with various Azure services Orchestrate and ingest data using Azure Data Factory Learn how to use Azure Stream Analytics Get to know more about Synapse Analytics and its features Learn how to use Azure Analysis Services and its functionalities Ê WHO THIS BOOK IS FORÊ This book is for anyone who has basic to intermediate knowledge of cloud and analytics concepts and wants to use Microsoft Azure for Data Analytics. This book will also benefit Data Scientists who want to use Azure for Machine Learning. Ê TABLE OF CONTENTSÊÊ 1. Ê Data and its power 2. Ê Evolution of Analytics and its Types 3. Ê Internet of Things 4. Ê AI and ML 5. Ê Why cloud 6. Ê What are a data lake and a modern datamart 7. Ê Introduction to Azure services 8. Ê Types of data 9. Ê Azure Data Factory 10. Stream Analytics 11. Azure Data Lake Store and Azure Storage 12. Cosmos DB 13.Ê Synapse Analytics 14.Ê Azure Databricks 15.Ê Azure Analysis Services 16.Ê Power BI 17.Ê Azure Machine Learning 18.Ê Sample Architectures and synergies - Real-Time and Batch 19.Ê Azure Data Catalog 20.Ê Azure Active Directory 21.Ê Azure Webapps 22.Ê Power apps 23.Ê Time Series Insights 24.Ê Azure Cognitive Services 25.Ê Azure Logicapps 26.Ê Azure VM 27.Ê Azure Functions 28.Ê Azure Containers 29.Ê Azure KubernetesÊ Service 30.Ê Use Case 1 31.Ê Use Case 2
  azure data science vm: Deep Learning with Azure Mathew Salvaris, Danielle Dean, Wee Hyong Tok, 2018-08-24 Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.
  azure data science vm: Understanding Azure Data Factory Sudhir Rawat, Abhishek Narain, 2018-12-18 Improve your analytics and data platform to solve major challenges, including operationalizing big data and advanced analytics workloads on Azure. You will learn how to monitor complex pipelines, set alerts, and extend your organization's custom monitoring requirements. This book starts with an overview of the Azure Data Factory as a hybrid ETL/ELT orchestration service on Azure. The book then dives into data movement and the connectivity capability of Azure Data Factory. You will learn about the support for hybrid data integration from disparate sources such as on-premise, cloud, or from SaaS applications. Detailed guidance is provided on how to transform data and on control flow. Demonstration of operationalizing the pipelines and ETL with SSIS is included. You will know how to leverage Azure Data Factory to run existing SSIS packages. As you advance through the book, you will wrap up by learning how to create a single pane for end-to-end monitoring, which is a key skill in building advanced analytics and big data pipelines. What You'll LearnUnderstand data integration on Azure cloudBuild and operationalize an ADF pipelineModernize a data warehouseBe aware of performance and security considerations while moving data Who This Book Is ForData engineers and big data developers. ETL (extract, transform, load) developers also will find the book useful in demonstrating various operations.
  azure data science vm: Azure Data Scientist Associate Certification Guide Andreas Botsikas, Michael Hlobil, 2021-12-03 Develop the skills you need to run machine learning workloads in Azure and pass the DP-100 exam with ease Key FeaturesCreate end-to-end machine learning training pipelines, with or without codeTrack experiment progress using the cloud-based MLflow-compatible process of Azure ML servicesOperationalize your machine learning models by creating batch and real-time endpointsBook Description The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam. What you will learnCreate a working environment for data science workloads on AzureRun data experiments using Azure Machine Learning servicesCreate training and inference pipelines using the designer or codeDiscover the best model for your dataset using Automated MLUse hyperparameter tuning to optimize trained modelsDeploy, use, and monitor models in productionInterpret the predictions of a trained modelWho this book is for This book is for developers who want to infuse their applications with AI capabilities and data scientists looking to scale their machine learning experiments in the Azure cloud. Basic knowledge of Python is needed to follow the code samples used in the book. Some experience in training machine learning models in Python using common frameworks like scikit-learn will help you understand the content more easily.
  azure data science vm: SQL Server on Azure Virtual Machines Joey D'Antoni, Louis Davidson, Allan Hirt, John Martin, Anthony Nocentino, Tim Radney, Randolph West, 2020-06-04 Learn how to combine SQL Server's analytics with Azure's flexibility and hybrid connectivity to achieve industry-leading performance and manageability for your cloud database. Key FeaturesUnderstand platform availability for SQL Server in AzureExplore the benefits and deployment choices offered by SQL IaaSGet to grips with deploying SQL Server on the Linux development ecosystemBook Description Deploying SQL Server on Azure virtual machines allows you to work on full versions of SQL Server in the cloud without having to maintain on-premises hardware. The book begins by introducing you to the SQL portfolio in Azure and takes you through SQL Server IaaS scenarios, before explaining the factors that you need to consider while choosing an OS for SQL Server in Azure VMs. As you progress through the book, you'll explore different VM options and deployment choices for IaaS and understand platform availability, migration tools, and best practices in Azure. In later chapters, you'll learn how to configure storage to achieve optimized performance. Finally, you'll get to grips with the concept of Azure Hybrid Benefit and find out how you can use it to maximize the value of your existing on-premises SQL Server. By the end of this book, you'll be proficient in administering SQL Server on Microsoft Azure and leveraging the tools required for its deployment. What you will learnChoose an operating system for SQL Server in Azure VMsUse the Azure Management Portal to facilitate the deployment processVerify connectivity and network latency in cloudConfigure storage for optimal performance and connectivityExplore various disaster recovery options for SQL Server in AzureOptimize SQL Server on LinuxDiscover how to back up databases to a URLWho this book is for SQL Server on Azure VMs is for you if you are a developer, data enthusiast, or anyone who wants to migrate SQL Server databases to Azure virtual machines. Basic familiarity with SQL Server and managed identities for Azure resources will be a plus.
  azure data science vm: Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning Ginger Grant, Julio Granados, Guillermo Fernandez, Pau Sempere, Javier Torrenteras, Paco Gonzalez, Tamanaco Francísquez, 2018-03-01 Prepare for Microsoft Exam 70-774–and help demonstrate your real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: Prepare data for analysis in Azure Machine Learning and export from Azure Machine Learning Develop machine learning models Operationalize and manage Azure Machine Learning Services Use other services for machine learning This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you are familiar with Azure data services, machine learning concepts, and common data science processes About the Exam Exam 70-774 focuses on skills and knowledge needed to prepare data for analysis with Azure Machine Learning; find key variables describing your data’s behavior; develop models and identify optimal algorithms; train, validate, deploy, manage, and consume Azure Machine Learning Models; and leverage related services and APIs. About Microsoft Certification Passing this exam as well as Exam 70-773: Analyzing Big Data with Microsoft R earns your MCSA: Machine Learning certifi¿cation, demonstrating your expertise in operationalizing Microsoft Azure machine learning and Big Data with R Server and SQL R Services. See full details at: microsoft.com/learning
  azure data science vm: Machine Learning with Microsoft Technologies Leila Etaati, 2019-06-12 Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today’s game changer and should be a key building block in every company’s strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set. Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements. Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages, the main languages inside Microsoft technologies. What You'll Learn Choose the right Microsoft product for your machine learning solutionCreate and manage Microsoft’s tool environments for development, testing, and production of a machine learning projectImplement and deploy supervised and unsupervised learning in Microsoft products Set up Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench, Azure ML Server Developer, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools, and more Architect a machine learning solution factoring in all aspects of self service, enterprise, deployment, and sharing Who This Book Is For Data scientists, data analysts, developers, architects, and managers who want to leverage machine learning in their products, organization, and services, and make educated, cost-saving decisions about their ML architecture and tool set.
  azure data science vm: Azure and Xamarin Forms Russell Fustino, 2018-06-15 Discover how to create cross platform apps for Android, iOS and UWP using Azure services and C# with Xamarin Forms. This book illustrates how to utilize Azure cloud storage for serving up Azure SQL DB data through Azure App Services. The book starts by setting up Xamarin and introducing Xamarin Forms and then covers the Azure Portal from a developer’s perspective and goes on to demonstrate how to build an Azure Service using Quickstart. You'll also see how to add Azure support to Xamarin Forms application. You'll review in detail how to build a Xamarin Form with Azure Client and modify an existing app to become a Xamarin Forms Client for Azure with offline synchronization. You then move on to third-party controls that speed up development. By the end of the book, you will be able to use Azure and Xamarin together and master how to use Azure Mobile Quickstarts, Azure SQL plumbing, database synchronization and Xamarin Forms. What You'll Learn Create a Xamarin Forms App and understand the Structure of a Xamarin Forms App. Navigate pages and use platform specific coding. Use images, ListView and the Azure Mobile App Quickstart to build a Service and Xamarin Forms app Modify an existing app to use Azure Client Libraries, understand offline storage with SQLite and incorporate offline synchronization Who This Book Is For Software developers new to Xamarin and/or Azure and for the developers who are familiar with both the technologies to use in mobile apps.
  azure data science vm: Introduction to Data Platforms Anthony David Giordano, 2022-11-03 Digital, cloud, and artificial intelligence (AI) have disrupted how we use data. This disruption has changed the way we need to provision, curate, and publish data for the multiple use cases in today's technology-driven environment. This text will cover how to design, develop, and evolve a data platform for all the uses of enterprise data needed in today's digital organization. This book focuses on explaining what a data platform is, what value it provides, how is it engineered, and how to deploy a data platform and support organization. In this context, Introduction to Data Platforms reviews the current requirements for data in the digital age and quantifies the use cases; discusses the evolution of data over the past twenty years, which is a core driver of the modern data platform; defines what a data platform is and defines the architectural components and layers of a data platform; provides the architectural layers or capabilities of a data platform; reviews cloud- and commercial-software vendors that populate the data-platform space; provides a step-by-step approach to engineering, deploying, supporting, and evolving a data-platform environment; provides a step-by-step approach to migrating legacy data warehouses, data marts, and data lakes/sandboxes to a data platform; and reviews organizational structures for managing data platform environments.
  azure data science vm: Cognitive Computing Recipes Adnan Masood, Adnan Hashmi, 2019-03-27 Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes deep learning and machine learning accessible to everyday developers, by providing a combination of tools such as cognitive services APIs, machine learning platforms, and libraries. Along with an overview of the contemporary technology landscape, Machine Learning and Deep Learning with Cognitive Computing Recipes covers the business case for machine learning and deep learning. Covering topics such as digital assistants, computer vision, text analytics, speech, and robotics process automation this book offers a comprehensive toolkit that you can apply quickly and easily in your own projects. With its focus on Microsoft Cognitive Services offerings, you’ll see recipes using multiple different environments including TensowFlow and CNTK to give you a broader perspective of the deep learning ecosystem. What You Will LearnBuild production-ready solutions using Microsoft Cognitive Services APIs Apply deep learning using TensorFlow and Microsoft Cognitive Toolkit (CNTK) Solve enterprise problems in natural language processing and computer vision Discover the machine learning development life cycle – from formal problem definition to deployment at scale Who This Book Is For Software engineers and enterprise architects who wish to understand machine learning and deep learning by building applications and solving real-world business problems.
  azure data science vm: Big Data Infrastructure Technologies for Data Analytics Yuri Demchenko,
  azure data science vm: Exam Prep AZ-305 Lalit Rawat, 2024-07-24 DESCRIPTION “Exam Prep AZ-305: Designing Microsoft Azure Infrastructure Solutions” book is a comprehensive guide for IT professionals preparing for the Microsoft Azure AZ-305 certification exam. This book offers detailed insights into designing scalable, secure, and resilient infrastructure solutions on the Azure platform, aligning with the latest exam objectives. It covers critical topics such as designing governance, security, storage, and networking solutions, ensuring readers have the necessary knowledge to architect effective Azure solutions. Through a blend of theoretical concepts and practical exercises, this guide equips readers with the skills needed to apply Azure best practices in real-world scenarios. Each chapter covers specific areas of infrastructure design, providing step-by-step instructions, expert tips, and real-life examples to illustrate complex concepts. This practical approach not only helps in mastering the exam content but also enhances the reader's ability to solve real-world challenges in their job roles. It not only prepares you for certification but also empowers you to design and implement robust Azure infrastructure solutions, thereby enhancing your capabilities and career prospects in the evolving field of cloud technology. KEY FEATURES ● Expertise in Azure networking, storage, compute, identity management, monitoring, security, hybrid cloud solutions, and disaster recovery. ● Learn to design and implement robust Azure infrastructure solutions. ● Prepare for the AZ-305 Azure Infrastructure Architect certification exam. ● Utilize up-to-date Microsoft AZ-305 curriculum. WHAT YOU WILL LEARN ● Master Azure governance principles. ● Design secure authentication and authorization solutions. ● Architect scalable compute solutions on Azure. ● Implement effective data storage and integration strategies. ● Design robust backup and disaster recovery solutions. ● Learn key migration strategies for transitioning to Azure. WHO THIS BOOK IS FOR Whether you are an aspiring cloud architect, a seasoned IT professional, or someone looking to advance their career in cloud computing, this book serves as an essential resource. TABLE OF CONTENTS 1. Designing Governance 2. Designing Authentication and Authorization Solutions 3. Designing a Solution Monitor of Azure Resources 4. Designing an Azure Compute Solution 5. Designing a Data Storage Solution for Non-relational Data 6. Designing Data Integration 7. Designing Data Storage Solutions for Relational Data 8. Designing Network Solutions 9. Designing a Solution for Backup and Disaster Recovery 10. Designing Migration 11. Azure Well-Architected Framework 12. Exam Preparation Guidelines and Assessment Questions 13. Azure Architect Exam Mock Test
  azure data science vm: Beginning Azure IoT Edge Computing David Jensen, 2019-04-29 Use a step-by-step process to create and deploy your first Azure IoT Edge solution. Modern day developers and architects in today’s cloud-focused world must understand when it makes sense to leverage the cloud. Computing on the edge is a new paradigm for most people. The Azure IoT Edge platform uses many existing technologies that may be familiar to developers, but understanding how to leverage those technologies in an edge computing scenario can be challenging. Beginning Azure IoT Edge Computing demystifies computing on the edge and explains, through concrete examples and exercises, how and when to leverage the power of intelligent edge computing. It introduces the possibilities of intelligent edge computing using the Azure IoT Edge platform, and guides you through hands-on exercises to make edge computing approachable, understandable, and highly useful. Through user-friendlydiscussion you will not only understand how to build edge solutions, but also when to build them. By explaining some common solution patterns, the decision on when to use the cloud and when to avoid the cloud will become much clearer. What You'll Learn Create and deploy Azure IoT Edge solutions Recognize when to leverage the intelligent edge pattern and when to avoid it Leverage the available developer tooling to develop and debug IoT Edge solutions Know which off-the-shelf edge computing modules are available Become familiar with some of the lesser-known device protocols used in conjunction with edge computing Understand how to securely deploy and bootstrap an IoT Edge deviceExplore related topics such as containers and secure device provisioning Who This Book Is For Developers or architects who want to understand edge computing and when and where to use it. Readers should be familiar with C# or Python and have a high-level understanding of the Azure IoT platform.
  azure data science vm: Microsoft Azure AI Fundamentals AI-900 Exam Guide Aaron Guilmette, Steve Miles, 2024-05-31 Get ready to pass the certification exam on your first attempt by gaining actionable insights into AI concepts, ML techniques, and Azure AI services covered in the latest AI-900 exam syllabus from two industry experts Key Features Discover Azure AI services, including computer vision, Auto ML, NLP, and OpenAI Explore AI use cases, such as image identification, chatbots, and more Work through 145 practice questions under chapter-end self-assessments and mock exams Purchase of this book unlocks access to web-based exam prep resources, including mock exams, flashcards, and exam tips Book Description The AI-900 exam helps you take your first step into an AI-shaped future. Regardless of your technical background, this book will help you test your understanding of the key AI-related topics and tools used to develop AI solutions in Azure cloud. This exam guide focuses on AI workloads, including natural language processing (NLP) and large language models (LLMs). You'll explore Microsoft's responsible AI principles like safety and accountability. Then, you'll cover the basics of machine learning (ML), including classification and deep learning, and learn how to use training and validation datasets with Azure ML. Using Azure AI Vision, face detection, and Video Indexer services, you'll get up to speed with computer vision-related topics like image classification, object detection, and facial detection. Later chapters cover NLP features such as key phrase extraction, sentiment analysis, and speech processing using Azure AI Language, speech, and translator services. The book also guides you through identifying GenAI models and leveraging Azure OpenAI Service for content generation. At the end of each chapter, you'll find chapter review questions with answers, provided as an online resource. By the end of this exam guide, you'll be able to work with AI solutions in Azure and pass the AI-900 exam using the online exam prep resources. What you will learn Discover various types of artificial intelligence (AI)workloads and services in Azure Cover Microsoft's guiding principles for responsible AI development and use Understand the fundamental principles of how AI and machine learning work Explore how AI models can recognize content in images and documents Gain insights into the features and use cases for natural language processing Explore the capabilities of generative AI services Who this book is for Whether you're a cloud engineer, software developer, an aspiring data scientist, or simply interested in learning AI/ML concepts and capabilities on Azure, this book is for you. The book also serves as a foundation for those looking to attempt more advanced AI and data science-related certification exams (e.g. Microsoft Certified: Azure AI Engineer Associate). Although no experience in data science and software engineering is required, basic knowledge of cloud concepts and client-server applications is assumed.
  azure data science vm: Mastering PyTorch Ashish Ranjan Jha, 2024-05-31 Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Understand how to use PyTorch to build advanced neural network models Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks Book DescriptionPyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text, vision, and music generation models using PyTorch Build a deep Q-network (DQN) model in PyTorch Deploy PyTorch models on mobile devices (Android and iOS) Become well versed in rapid prototyping using PyTorch with fastai Perform neural architecture search effectively using AutoML Easily interpret machine learning models using Captum Design ResNets, LSTMs, and graph neural networks (GNNs) Create language and vision transformer models using Hugging Face Who this book is for This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.
  azure data science vm: Migrating Applications to the Cloud with Azure Sjoukje Zaal, Amit Malik, Sander Rossel, Jason Marston, Mohamed Wali, Stefano Demiliani, 2019-12-06 Modernize your apps with Microsoft Azure by moving web, desktop, and mobile apps to the cloud Key FeaturesDecide which migration strategy is most suitable for your organization and create a migration roadmapMove existing infrastructure to Azure and learn strategies to reduce cost, increase storage, and improve ROIDesign secure, scalable, and cost-effective solutions with the help of practical examplesBook Description Whether you are trying to re-architect a legacy app or build a cloud-ready app from scratch, using the Azure ecosystem with .NET and Java technologies helps you to strategize and plan your app modernization process effectively. With this book, you’ll learn how to modernize your applications by using Azure for containerization, DevOps, microservices, and serverless solutions to reduce development time and costs, while also making your applications robust, secure, and scalable. You will delve into improving application efficiency by using container services such as Azure Container Service, Azure Kubernetes Service (AKS), and more. Next, you will learn to modernize your application by implementing DevOps throughout your application development life cycle. You will then focus on increasing the scalability and performance of your overall application with microservices, before learning how to add extra functionality to your application with Azure serverless solutions. Finally, you’ll get up to speed with monitoring and troubleshooting techniques. By the end of this book, you will have learned how to use the Azure ecosystem to refactor, re-architect, and rebuild your web, mobile, and desktop applications. What you will learnUse DevOps and containerization technologies to modernize your applications and infrastructureBuild microservices using Azure Service FabricDevelop scalable applications using Azure FunctionsManage and deploy your application code and database connectivitySecure and monitor your applications in Azure effectivelyDesign for high availability and disaster recoveryWho this book is for This book is for.NET and Java developers who want to modernize their applications using Azure. Solution architects and experienced developers interested in modernizing legacy applications using Azure will also find this book useful. Some prior understanding of cloud computing concepts will be beneficial.
  azure data science vm: Architecting Microsoft Azure Solutions – Exam Guide 70-535 Sjoukje Zaal, 2018-04-27 Get certified as an Azure architect by acing the 70-535 Architecting Microsoft Solutions (70-535) exam using this comprehensive guide with full coverage of the exam objectives Key Features Learn to successfully design and architect powerful solutions on the Azure Cloud platform Enhance your skills with mock tests and practice questions A detailed certification guide that will help you ace the 70-535 exam with confidence Book Description Architecting Microsoft Azure Solutions: Exam Guide 70-535 will get Azure architects and developers up-to-date with the latest updates on Azure from an architecture and design perspective. The book includes all the topics that are still relevant from the previous 70-534 exam, and is updated with latest topics covered, including Artificial Intelligence, IoT, and architecture styles. This exam guide is divided into six parts, where the first part will give you a good understanding of how to design a compute infrastructure. It also dives into designing networking and data implementations. You will learn about designing solutions for Platform Service and operations. Next, you will be able to secure your resources and data, as well as design a mechanism for governance and policies. You will also understand the objective of designing solutions for Platform Services, by covering Artificial Intelligence, IoT, media services, and messaging solution concepts. Finally, you will cover the designing for operations objective. This objective covers application and platform monitoring, as well as designing alerting strategies and operations automation strategies. By the end of the book, you’ll have met all of the exam objectives, and will have all the information you need to ace the 70-535 exam. You will also have become an expert in designing solutions on Microsoft Azure. What you will learn Use Azure Virtual Machines to design effective VM deployments Implement architecture styles, like serverless computing and microservices Secure your data using different security features and design effective security strategies Design Azure storage solutions using various storage features Create identity management solutions for your applications and resources Architect state-of-the-art solutions using Artificial Intelligence, IoT, and Azure Media Services Use different automation solutions that are incorporated in the Azure platform Who this book is for This book is for architects and experienced developers, who are gearing up for the 70-535 exam. Technical architects interested in learning more about designing Cloud solutions will also find this book useful.
  azure data science vm: C# 8 and .NET Core 3 Projects Using Azure Paul Michaels, Dirk Strauss, Jas Rademeyer, 2019-12-31 Get up to speed with using C# 8 and .NET Core 3.0 features to build real-world .NET Core applications Key FeaturesLearn the core concepts of web applications, serverless computing, and microservicesCreate an ASP.NET Core MVC application using controllers, routing, middleware and authenticationBuild modern applications using cutting-edge services from Microsoft AzureBook Description .NET Core is a general-purpose, modular, cross-platform, and opensource implementation of .NET. The latest release of .NET Core 3 comes with improved performance and security features, along with support for desktop applications. .NET Core 3 is not only useful for new developers looking to start learning the framework, but also for legacy developers interested in migrating their apps. Updated with the latest features and enhancements, this updated second edition is a step-by-step, project-based guide. The book starts with a brief introduction to the key features of C# 8 and .NET Core 3. You'll learn to work with relational data using Entity Framework Core 3, before understanding how to use ASP.NET Core. As you progress, you’ll discover how you can use .NET Core to create cross-platform applications. Later, the book will show you how to upgrade your old WinForms apps to .NET Core 3. The concluding chapters will then help you use SignalR effectively to add real-time functionality to your applications, before demonstrating how to implement MongoDB in your apps. Finally, you'll delve into serverless computing and how to build microservices using Docker and Kubernetes. By the end of this book, you'll be proficient in developing applications using .NET Core 3. What you will learnUnderstand how to incorporate the Entity Framework Core 3 to build ASP.NET Core MVC applicationsCreate a real-time chat application using Azure’s SignalR serviceGain hands-on experience of working with Cosmos DBDevelop an Azure Function and interface it with an Azure Logic AppExplore user authentication with Identity Server and OAuth2Understand how to use Azure Cognitive Services to add advanced functionalities with minimal codeGet to grips with running a .NET Core application with KubernetesWho this book is for This book is for developers and programmers of all levels who want to build real-world projects and explore the new features of .NET Core 3. Developers working on legacy desktop software who are looking to migrate to .NET Core 3 will also find this book useful. Basic knowledge of .NET Core and C# is assumed.
  azure data science vm: Hands-On Cloud Solutions with Azure Greg Leonardo, 2018-10-31 Design effective Azure architecture and transform your IT business solutions Key FeaturesDevelop a resilient and robust cloud environmentDeploy and manage cost-effective and highly available solutions on your public cloudDesign and implement enterprise-level cloud solutionsBook Description Azure provides cloud-based solutions to support your business demands. Building and running solutions on Azure will help your business maximize the return on investment and minimize the total cost of ownership. Hands-On Cloud Solutions with Azure focuses on addressing the architectural decisions that usually arise when you design or migrate a solution to Microsoft Azure. You will start by designing the building blocks of infrastructure solution on Azure, such as Azure compute, storage, and networking, followed by exploring the database options it offers. You will get to grips with designing scalable web and mobile solutions and understand where to host your Active Directory and Identity Solution. Moving on, you’ll learn how to extend DevOps to Azure. You will also beneft from some exciting services that enable extremely smooth operations and streamlined DevOps between on-premises and cloud. The book will help you to design a secure environment for your solution, on both the Cloud and hybrid. Toward the end, you’ll see how to manage and monitor cloud and hybrid solutions. By the end of this book, you will be armed with all the tools and knowledge you need to properly plan and design your solutions on Azure, whether it’s for a brand new project or migration project. What you will learnGet started with Azure by understanding tenants, subs, and resource groupsDecide whether to “lift and shift” or migrate appsPlan and architect solutions in AzureBuild ARM templates for Azure resourcesDevelop and deploy solutions in AzureUnderstand how to monitor and support your application with AzureMake your life easier with Azure best practices and tipsWho this book is for If you’re an IT consultant, developer, or solutions architect looking to design effective solutions for your organization, this book is for you. Some knowledge of cloud computing will assist with understanding the key concepts covered in this book.
  azure data science vm: Genomics in the Azure Cloud Colby T. Ford, 2022-11-14 This practical guide bridges the gap between general cloud computing architecture in Microsoft Azure and scientific computing for bioinformatics and genomics. You'll get a solid understanding of the architecture patterns and services that are offered in Azure and how they might be used in your bioinformatics practice. You'll get code examples that you can reuse for your specific needs. And you'll get plenty of concrete examples to illustrate how a given service is used in a bioinformatics context. You'll also get valuable advice on how to: Use enterprise platform services to easily scale your bioinformatics workloads Organize, query, and analyze genomic data at scale Build a genomics data lake and accompanying data warehouse Use Azure Machine Learning to scale your model training, track model performance, and deploy winning models Orchestrate and automate processing pipelines using Azure Data Factory and Databricks Cloudify your organization's existing bioinformatics pipelines by moving your workflows to Azure high-performance compute services And more
  azure data science vm: Exam DP-100: Azure Data Scientist Associate 48 Test Prep Questions Ger Arevalo, 2019-08-29 This book is designed to be an ancillary to the classes, labs, and hands on practice that you have diligently worked on in preparing to obtain your DP-100: Azure Data Scientist Associate certification. I won’t bother talking about the benefits of certifications. This book tries to reinforce the knowledge that you have gained in your process of studying. It is meant as one of the end steps in your preparation for the DP-100 exam. This book is short, but It will give you a good gauge of your readiness. Learning can be seen in 4 stages: 1. Unconscious Incompetence 2. Conscious Incompetence 3. Conscious Competence 4. Unconscious Competence This book will assume the reader has already gone through the needed classes, labs, and practice. It is meant to take the reader from stage 2, Conscious Incompetence, to stage 3 Conscious Competence. At stage 3, you should be ready to take the exam. Only real-world scenarios and work experience will take you to stage 4, Unconscious Competence. Before we get started, we all have doubts when preparing to take an exam. What is your reason and purpose for taking this exam? Remember your reason and purpose when you have some doubts. Obstacle is the way. Control your mind, attitude, and you can control the situation. Persistence leads to confidence. Confidence erases doubts.
  azure data science vm: Exam Ref DP-100 Designing and Implementing a Data Science Solution on Azure Dayne Sorvisto, 2024-12-06 Prepare for Microsoft Exam DP-100 and demonstrate your real-world knowledge of managing data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Python, Azure Machine Learning, and MLflow. Designed for professionals with data science experience, this Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified: Azure Data Scientist Associate level. Focus on the expertise measured by these objectives: Design and prepare a machine learning solution Explore data and train models Prepare a model for deployment Deploy and retrain a model This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you have experience in designing and creating a suitable working environment for data science workloads, training machine learning models, and managing, deploying, and monitoring scalable machine learning solutions About the Exam Exam DP-100 focuses on knowledge needed to design and prepare a machine learning solution, manage an Azure Machine Learning workspace, explore data and train models, create models by using the Azure Machine Learning designer, prepare a model for deployment, manage models in Azure Machine Learning, deploy and retrain a model, and apply machine learning operations (MLOps) practices. About Microsoft Certification Passing this exam fulfills your requirements for the Microsoft Certified: Azure Data Scientist Associate credential, demonstrating your expertise in applying data science and machine learning to implement and run machine learning workloads on Azure, including knowledge and experience using Azure Machine Learning and MLflow.
  azure data science vm: Data Science and Security Samiksha Shukla, Xiao-Zhi Gao, Joseph Varghese Kureethara, Durgesh Mishra, 2022-07-01 This book presents best selected papers presented at the International Conference on Data Science for Computational Security (IDSCS 2022), organized by the Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus, India, during 11 – 12 February 2022. The book proposes new technologies and discusses future solutions and applications of data science, data analytics and security. The book targets current research works in the areas of data science, data security, data analytics, artificial intelligence, machine learning, computer vision, algorithms design, computer networking, data mining, big data, text mining, knowledge representation, soft computing and cloud computing.
  azure data science vm: Mastering Azure Machine Learning Christoph Körner, Kaijisse Waaijer, 2020-04-30 Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes Key FeaturesMake sense of data on the cloud by implementing advanced analyticsTrain and optimize advanced deep learning models efficiently on Spark using Azure DatabricksDeploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)Book Description The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure. What you will learnSetup your Azure Machine Learning workspace for data experimentation and visualizationPerform ETL, data preparation, and feature extraction using Azure best practicesImplement advanced feature extraction using NLP and word embeddingsTrain gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure Machine LearningUse hyperparameter tuning and Azure Automated Machine Learning to optimize your ML modelsEmploy distributed ML on GPU clusters using Horovod in Azure Machine LearningDeploy, operate and manage your ML models at scaleAutomated your end-to-end ML process as CI/CD pipelines for MLOpsWho this book is for This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.
  azure data science vm: Mastering Windows Server 2016 Hyper-V John Savill, 2016-12-19 Build a seamless, flexible, full-service datacenter solution Microsoft Windows Server 2016 Hyper-V is the IT administrator's guide to this rising datacenter solution. Hyper-V has already surpassed VMWare in datacenter management, identity service for multiple devices, and more; this book shows you how to harness the power of this hypervisor to simplify the infrastructure, reduce costs, improve productivity, and better manage system resources. From a tour of the technology through architecture, deployment, and integration of System Center, Microsoft Azure, and Microsoft Azure Stack, the discussion illustrates the skills you need to create a complete solution for optimum enterprise management. Coverage includes Windows Azure capabilities for virtual machines, managing a hybrid cloud, IaaS, storage capabilities, PowerShell, and more, with practical real-world guidance from a leading authority in the field. Hyper-V has recently undergone improvements in scalability and features that have positioned it as an ideal solution in the Small/Medium Business and Enterprise markets. This book shows you how to exploit these new capabilities to build a robust data solution for your organization. Discover the capabilities of Microsoft Hyper-V Architect a Hyper-V datacenter solution Plan and manage a deployment or migration Integrate complementary technologies for full scalability Data is everywhere—on desktops, laptops, phones, and multiple operating systems, accessed through email, text messages, web searches, online services, and more. All of this data must be stored, accessible, updated, backed up, secured, managed, sorted, and analyzed—sometimes instantly. Hyper-V is the rising star in the virtualization space, and Microsoft Windows Server 2016 Hyper-V shows you how to turn greater capabilities into better datacenter solutions.
  azure data science vm: Hands-On Data Science with SQL Server 2017 Marek Chmel, Vladimír Mužný, 2018-11-29 Find, explore, and extract big data to transform into actionable insights Key FeaturesPerform end-to-end data analysis—from exploration to visualizationReal-world examples, tasks, and interview queries to be a proficient data scientistUnderstand how SQL is used for big data processing using HiveQL and SparkSQLBook Description SQL Server is a relational database management system that enables you to cover end-to-end data science processes using various inbuilt services and features. Hands-On Data Science with SQL Server 2017 starts with an overview of data science with SQL to understand the core tasks in data science. You will learn intermediate-to-advanced level concepts to perform analytical tasks on data using SQL Server. The book has a unique approach, covering best practices, tasks, and challenges to test your abilities at the end of each chapter. You will explore the ins and outs of performing various key tasks such as data collection, cleaning, manipulation, aggregations, and filtering techniques. As you make your way through the chapters, you will turn raw data into actionable insights by wrangling and extracting data from databases using T-SQL. You will get to grips with preparing and presenting data in a meaningful way, using Power BI to reveal hidden patterns. In the concluding chapters, you will work with SQL Server integration services to transform data into a useful format and delve into advanced examples covering machine learning concepts such as predictive analytics using real-world examples. By the end of this book, you will be in a position to handle the growing amounts of data and perform everyday activities that a data science professional performs. What you will learnUnderstand what data science is and how SQL Server is used for big data processingAnalyze incoming data with SQL queries and visualizationsCreate, train, and evaluate predictive modelsMake predictions using trained models and establish regular retraining coursesIncorporate data source querying into SQL ServerEnhance built-in T-SQL capabilities using SQLCLRVisualize data with Reporting Services, Power View, and Power BITransform data with R, Python, and AzureWho this book is for Hands-On Data Science with SQL Server 2017 is intended for data scientists, data analysts, and big data professionals who want to master their skills learning SQL and its applications. This book will be helpful even for beginners who want to build their career as data science professionals using the power of SQL Server 2017. Basic familiarity with SQL language will aid with understanding the concepts covered in this book.
  azure data science vm: Azure Data Factory Cookbook Dmitry Anoshin, Dmitry Foshin, Roman Storchak, Xenia Ireton, 2020-12-24 Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data Factory Key FeaturesLearn how to load and transform data from various sources, both on-premises and on cloudUse Azure Data Factory’s visual environment to build and manage hybrid ETL pipelinesDiscover how to prepare, transform, process, and enrich data to generate key insightsBook Description Azure Data Factory (ADF) is a modern data integration tool available on Microsoft Azure. This Azure Data Factory Cookbook helps you get up and running by showing you how to create and execute your first job in ADF. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines. This book will help you to discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage, which are frequently used for big data analytics. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premise infrastructure with cloud-native tools to get relevant business insights. As you advance, you’ll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. The book will take you through the common errors that you may encounter while working with ADF and show you how to use the Azure portal to monitor pipelines. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you’ll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects. What you will learnCreate an orchestration and transformation job in ADFDevelop, execute, and monitor data flows using Azure SynapseCreate big data pipelines using Azure Data Lake and ADFBuild a machine learning app with Apache Spark and ADFMigrate on-premises SSIS jobs to ADFIntegrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure FunctionsRun big data compute jobs within HDInsight and Azure DatabricksCopy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectorsWho this book is for This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You’ll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is expected.
  azure data science vm: Cloud Computing for Science and Engineering Ian Foster, Dennis B. Gannon, 2017-09-29 A guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security. The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors.
  azure data science vm: Azure Internet of Things Revealed Robert Stackowiak, 2019-11-09 Design, build, and justify an optimal Microsoft IoT footprint to meet your project needs. This book describes common Internet of Things components and architecture and then focuses on Microsoft’s Azure components relevant in deploying these solutions. Microsoft-specific topics addressed include: deploying edge devices and pushing intelligence to the edge; connecting IoT devices to Azure and landing data there, applying Azure Machine Learning, analytics, and Cognitive Services; roles for Microsoft solution accelerators and managed solutions; and integration of the Azure footprint with legacy infrastructure. The book concludes with a discussion of best practices in defining and developing solutions and creating a plan for success. What You Will LearnDesign the right IoT architecture to deliver solutions for a variety of project needs Connect IoT devices to Azure for data collection and delivery of services Use Azure Machine Learning and Cognitive Services to deliver intelligence in cloud-based solutions and at the edge Understand the benefits and tradeoffs of Microsoft's solution accelerators and managed solutions Investigate new use cases that are described and apply best practices in deployment strategies Integrate cutting-edge Azure deployments with existing legacy data sources Who This Book Is For Developers and architects new to IoT projects or new to Microsoft Azure IoT components as well as readers interested in best practices used in architecting IoT solutions that utilize the Azure platform
  azure data science vm: MCA Microsoft Certified Associate Azure Data Engineer Study Guide Benjamin Perkins, 2023-08-02 Prepare for the Azure Data Engineering certification—and an exciting new career in analytics—with this must-have study aide In the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203, accomplished data engineer and tech educator Benjamin Perkins delivers a hands-on, practical guide to preparing for the challenging Azure Data Engineer certification and for a new career in an exciting and growing field of tech. In the book, you’ll explore all the objectives covered on the DP-203 exam while learning the job roles and responsibilities of a newly minted Azure data engineer. From integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions, you’ll get up to speed quickly and efficiently with Sybex’s easy-to-use study aids and tools. This Study Guide also offers: Career-ready advice for anyone hoping to ace their first data engineering job interview and excel in their first day in the field Indispensable tips and tricks to familiarize yourself with the DP-203 exam structure and help reduce test anxiety Complimentary access to Sybex’s expansive online study tools, accessible across multiple devices, and offering access to hundreds of bonus practice questions, electronic flashcards, and a searchable, digital glossary of key terms A one-of-a-kind study aid designed to help you get straight to the crucial material you need to succeed on the exam and on the job, the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203 belongs on the bookshelves of anyone hoping to increase their data analytics skills, advance their data engineering career with an in-demand certification, or hoping to make a career change into a popular new area of tech.
  azure data science vm: Ultimate Azure Data Scientist Associate (DP-100) Certification Guide Rajib Kumar De, 2024-06-26 TAGLINE Empower Your Data Science Journey: From Exploration to Certification in Azure Machine Learning KEY FEATURES ● Offers deep dives into key areas such as data preparation, model training, and deployment, ensuring you master each concept. ● Covers all exam objectives in detail, ensuring a thorough understanding of each topic required for the DP-100 certification. ● Includes hands-on labs and practical examples to help you apply theoretical knowledge to real-world scenarios, enhancing your learning experience. DESCRIPTION Ultimate Azure Data Scientist Associate (DP-100) Certification Guide is your essential resource for achieving the Microsoft Azure Data Scientist Associate certification. This guide covers all exam objectives, helping you design and prepare machine learning solutions, explore data, train models, and manage deployment and retraining processes. The book starts with the basics and advances through hands-on exercises and real-world projects, to help you gain practical experience with Azure's tools and services. The book features certification-oriented Q&A challenges that mirror the actual exam, with detailed explanations to help you thoroughly grasp each topic. Perfect for aspiring data scientists, IT professionals, and analysts, this comprehensive guide equips you with the expertise to excel in the DP-100 exam and advance your data science career. WHAT WILL YOU LEARN ● Design and prepare effective machine learning solutions in Microsoft Azure. ● Learn to develop complete machine learning training pipelines, with or without code. ● Explore data, train models, and validate ML pipelines efficiently. ● Deploy, manage, and optimize machine learning models in Azure. ● Utilize Azure's suite of data science tools and services, including Prompt Flow, Model Catalog, and AI Studio. ● Apply real-world data science techniques to business problems. ● Confidently tackle DP-100 certification exam questions and scenarios. WHO IS THIS BOOK FOR? This book is for aspiring Data Scientists, IT Professionals, Developers, Data Analysts, Students, and Business Professionals aiming to Master Azure Data Science. Prior knowledge of basic Data Science concepts and programming, particularly in Python, will be beneficial for making the most of this comprehensive guide. TABLE OF CONTENTS 1. Introduction to Data Science and Azure 2. Setting Up Your Azure Environment 3. Data Ingestion and Storage in Azure 4. Data Transformation and Cleaning 5. Introduction to Machine Learning 6. Azure Machine Learning Studio 7. Model Deployment and Monitoring 8. Embracing AI Revolution Azure 9. Responsible AI and Ethics 10. Big Data Analytics with Azure 11. Real-World Applications and Case Studies 12. Conclusion and Next Steps Index
  azure data science vm: Serverless Integration Design Patterns with Azure Abhishek Kumar, Srinivasa Mahendrakar, 2019-02-13 A practical guide that helps you progress to using modern integration methods and leverage new cloud capability models Key FeaturesDesign critical hybrid integration solutions for your organizationGain in-depth knowledge of how to build cloud-native integration solutionsLeverage cognitive services to build smart cloud solutionsBook Description With more enterprises adapting cloud-based and API-based solutions, application integration has become more relevant and significant than ever before. Parallelly, Serverless Integration has gained popularity, as it helps agile organizations to build integration solutions quickly without having to worry about infrastructure costs. With Microsoft Azure’s serverless offerings, such as Logic Apps, Azure Functions, API Management, Azure Event Grid and Service Bus, organizations can build powerful, secure, and scalable integration solutions with ease. The primary objective of this book is to help you to understand various serverless offerings included within Azure Integration Services, taking you through the basics and industry practices and patterns. This book starts by explaining the concepts of services such as Azure Functions, Logic Apps, and Service Bus with hands-on examples and use cases. After getting to grips with the basics, you will be introduced to API Management and building B2B solutions using Logic Apps Enterprise Integration Pack. This book will help readers to understand building hybrid integration solutions and touches upon Microsoft Cognitive Services and leveraging them in modern integration solutions. Industry practices and patterns are brought to light at appropriate opportunities while explaining various concepts. What you will learnLearn about the design principles of Microsoft Azure Serverless IntegrationGet insights into Azure Functions, Logic Apps, Azure Event Grid and Service BusSecure and manage your integration endpoints using Azure API ManagementBuild advanced B2B solutions using Logic Apps, Enterprise Integration PackMonitor integration solutions using tools available on the marketDiscover design patterns for hybrid integrationWho this book is for Serverless Integration Design Patterns with Azure is for you if you are a solution architect or integration professional aiming to build complex cloud solutions for your organization. Developers looking to build next-level hybrid or cloud solutions will also find this book useful. Prior programming knowledge is necessary.
  azure data science vm: Mastering Microsoft Fabric Debananda Ghosh,
  azure data science vm: Building Cloud Data Platforms Solutions Anouar BEN ZAHRA, Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects.
  azure data science vm: Introducing Machine Learning Dino Esposito, Francesco Esposito, 2020-01-31 Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft’s powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning. · 14-time Microsoft MVP Dino Esposito and Francesco Esposito help you · Explore what’s known about how humans learn and how intelligent software is built · Discover which problems machine learning can address · Understand the machine learning pipeline: the steps leading to a deliverable model · Use AutoML to automatically select the best pipeline for any problem and dataset · Master ML.NET, implement its pipeline, and apply its tasks and algorithms · Explore the mathematical foundations of machine learning · Make predictions, improve decision-making, and apply probabilistic methods · Group data via classification and clustering · Learn the fundamentals of deep learning, including neural network design · Leverage AI cloud services to build better real-world solutions faster About This Book · For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills · Includes examples of machine learning coding scenarios built using the ML.NET library
  azure data science vm: R in Action, Third Edition Robert I. Kabacoff, 2022-06-28 R is the most powerful tool you can use for statistical analysis. This definitive guide smooths R’s steep learning curve with practical solutions and real-world applications for commercial environments. In R in Action, Third Edition you will learn how to: Set up and install R and RStudio Clean, manage, and analyze data with R Use the ggplot2 package for graphs and visualizations Solve data management problems using R functions Fit and interpret regression models Test hypotheses and estimate confidence Simplify complex multivariate data with principal components and exploratory factor analysis Make predictions using time series forecasting Create dynamic reports and stunning visualizations Techniques for debugging programs and creating packages R in Action, Third Edition makes learning R quick and easy. That’s why thousands of data scientists have chosen this guide to help them master the powerful language. Far from being a dry academic tome, every example you’ll encounter in this book is relevant to scientific and business developers, and helps you solve common data challenges. R expert Rob Kabacoff takes you on a crash course in statistics, from dealing with messy and incomplete data to creating stunning visualizations. This revised and expanded third edition contains fresh coverage of the new tidyverse approach to data analysis and R’s state-of-the-art graphing capabilities with the ggplot2 package. About the technology Used daily by data scientists, researchers, and quants of all types, R is the gold standard for statistical data analysis. This free and open source language includes packages for everything from advanced data visualization to deep learning. Instantly comfortable for mathematically minded users, R easily handles practical problems without forcing you to think like a software engineer. About the book R in Action, Third Edition teaches you how to do statistical analysis and data visualization using R and its popular tidyverse packages. In it, you’ll investigate real-world data challenges, including forecasting, data mining, and dynamic report writing. This revised third edition adds new coverage for graphing with ggplot2, along with examples for machine learning topics like clustering, classification, and time series analysis. What's inside Clean, manage, and analyze data Use the ggplot2 package for graphs and visualizations Techniques for debugging programs and creating packages A complete learning resource for R and tidyverse About the reader Requires basic math and statistics. No prior experience with R needed. About the author Dr. Robert I Kabacoff is a professor of quantitative analytics at Wesleyan University and a seasoned data scientist with more than 20 years of experience. Table of Contents PART 1 GETTING STARTED 1 Introduction to R 2 Creating a dataset 3 Basic data management 4 Getting started with graphs 5 Advanced data management PART 2 BASIC METHODS 6 Basic graphs 7 Basic statistics PART 3 INTERMEDIATE METHODS 8 Regression 9 Analysis of variance 10 Power analysis 11 Intermediate graphs 12 Resampling statistics and bootstrapping PART 4 ADVANCED METHODS 13 Generalized linear models 14 Principal components and factor analysis 15 Time series 16 Cluster analysis 17 Classification 18 Advanced methods for missing data PART 5 EXPANDING YOUR SKILLS 19 Advanced graphs 20 Advanced programming 21 Creating dynamic reports 22 Creating a package
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