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Author: Kaijisse Waaijer
Publisher:
Published: 2020-04-30
Total Pages: 394
ISBN-13: 9781789807554
DOWNLOAD EBOOKThis book will help you learn how to build a scalable end-to-end machine learning pipeline in Azure from experimentation and training to optimization and deployment. By the end of this book, you will learn to build complex distributed systems and scalable cloud infrastructure using powerful machine learning algorithms to compute insights.
Author: Christoph Körner
Publisher: Packt Publishing Ltd
Published: 2020-04-30
Total Pages: 437
ISBN-13: 1789801524
DOWNLOAD EBOOKMaster 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.
Author: Thomas K Abraham
Publisher: Packt Publishing Ltd
Published: 2018-10-31
Total Pages: 331
ISBN-13: 1789130271
DOWNLOAD EBOOKImplement 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
Author: Jeff Barnes
Publisher: Microsoft Press
Published: 2015-04-25
Total Pages: 393
ISBN-13: 073569818X
DOWNLOAD EBOOKMicrosoft 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.
Author: Christoph Korner
Publisher: Packt Publishing Ltd
Published: 2022-05-10
Total Pages: 624
ISBN-13: 1803246790
DOWNLOAD EBOOKSupercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services Key Features Implement end-to-end machine learning pipelines on Azure Train deep learning models using Azure compute infrastructure Deploy machine learning models using MLOps Book Description Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, you'll be able to combine all the steps you've learned by building an MLOps pipeline. What you will learn Understand the end-to-end ML pipeline Get to grips with the Azure Machine Learning workspace Ingest, analyze, and preprocess datasets for ML using the Azure cloud Train traditional and modern ML techniques efficiently using Azure ML Deploy ML models for batch and real-time scoring Understand model interoperability with ONNX Deploy ML models to FPGAs and Azure IoT Edge Build an automated MLOps pipeline using Azure DevOps Who this book is for This book is for machine learning engineers, data scientists, and machine learning developers who want to use the Microsoft Azure cloud to manage their datasets and machine learning experiments and build an enterprise-grade ML architecture using MLOps. This book will also help anyone interested in machine learning to explore important steps of the ML process and use Azure Machine Learning to support them, along with building powerful ML cloud applications. A basic understanding of Python and knowledge of machine learning are recommended.
Author: Zoiner Tejada
Publisher: "O'Reilly Media, Inc."
Published: 2017-04-06
Total Pages: 411
ISBN-13: 1491956623
DOWNLOAD EBOOKHelps users understand the breadth of Azure services by organizing them into a reference framework they can use when crafting their own big-data analytics solution.
Author: Deepak Mukunthu
Publisher: "O'Reilly Media, Inc."
Published: 2019-09-23
Total Pages: 190
ISBN-13: 1492055549
DOWNLOAD EBOOKDevelop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply automated machine learning (AutoML), a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology. Building machine-learning models is an iterative and time-consuming process. Even those who know how to create ML models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply AutoML to your data right away. Learn how companies in different industries are benefiting from AutoML Get started with AutoML using Azure Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning Understand how data analysts, BI professions, developers can use AutoML in their familiar tools and experiences Learn how to get started using AutoML for use cases including classification, regression, and forecasting.
Author: Dennis Michael Sawyers
Publisher: Packt Publishing Ltd
Published: 2021-04-23
Total Pages: 340
ISBN-13: 1800561970
DOWNLOAD EBOOKA practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language Key FeaturesCreate, deploy, productionalize, and scale automated machine learning solutions on Microsoft AzureImprove the accuracy of your ML models through automatic data featurization and model trainingIncrease productivity in your organization by using artificial intelligence to solve common problemsBook Description Automated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect. What you will learnUnderstand how to train classification, regression, and forecasting ML algorithms with Azure AutoMLPrepare data for Azure AutoML to ensure smooth model training and deploymentAdjust AutoML configuration settings to make your models as accurate as possibleDetermine when to use a batch-scoring solution versus a real-time scoring solutionProductionalize your AutoML and discover how to quickly deliver valueCreate real-time scoring solutions with AutoML and Azure Kubernetes ServiceTrain a large number of AutoML models at once using the AzureML Python SDKWho this book is for Data scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this machine learning book useful. You need to have beginner-level knowledge of artificial intelligence and a technical background in computer science, statistics, or information technology before getting started. Familiarity with Python will help you implement the more advanced features found in the chapters, but even data analysts and SQL experts will be able to train ML models after finishing this book.
Author: Ryan Mangan
Publisher: Packt Publishing Ltd
Published: 2024-07-26
Total Pages: 718
ISBN-13: 1835884156
DOWNLOAD EBOOKExplore the advanced capabilities of Azure Virtual Desktop and enhance your skills in cloud-based virtualization and remote application delivery Key Features Learn how to design a strong architecture for your Azure Virtual Desktop Implement, monitor, and maintain a virtual desktop environment Gain insights into Azure Virtual Desktop and prepare successfully for the AZ-140 exam Purchase of the print or Kindle book includes a free PDF eBook Book Description Acquire in-depth knowledge for designing, building, and supporting Azure Virtual Desktop environments with the updated second edition of Mastering Azure Virtual Desktop. With content aligned with exam objectives, this book will help you ace the Microsoft AZ-140 exam. This book starts with an introduction to Azure Virtual Desktop before delving into the intricacies of planning and architecting its infrastructure. As you progress, you’ll learn about the implementation process, with an emphasis on best practices and effective strategies. You’ll explore key areas such as managing and controlling access, advanced monitoring with the new Azure Monitoring Agent, and advanced application deployment. You’ll also gain hands-on experience with essential features like the MSIX app attach, enhancing user experience and operational efficiency. Beyond advancing your skills, this book is a crucial resource for those preparing for the Microsoft Certified: Azure Virtual Desktop Specialty certification. By the end of this book, you’ll have a thorough understanding of the Azure Virtual Desktop environment, from design to implementation. What you will learn Architect a robust Azure Virtual Desktop setup Master the essentials of networking and storage configurations Create and configure session host images and host pools Gain insights into controlling access and enhancing security Implement FSLogix profile containers and Cloud Cache for improved performance Discover MSIX app attach for efficient application delivery Understand strategies for business continuity and disaster recovery Monitor and manage the performance and health of your Azure Virtual Desktop environment Who this book is for Mastering Azure Virtual Desktop is for IT professionals, modern workspace administrators, architects, and consultants who want to learn how to design, implement, and manage Azure Virtual Desktop environments. Whether you're aiming to enhance your expertise in cloud virtualization or preparing for the Microsoft AZ-140 exam, this guide is an invaluable resource for advancing your skills.
Author: John Savill
Publisher: John Wiley & Sons
Published: 2015-04-20
Total Pages: 384
ISBN-13: 111900327X
DOWNLOAD EBOOKUnderstand, create, deploy, and maintain a public cloud using Microsoft Azure Mastering Microsoft Azure Infrastructure Services guides you through the process of creating and managing a public cloud and virtual network using Microsoft Azure. With step-by-step instruction and clear explanation, this book equips you with the skills required to provide services both on-premises and off-premises through full virtualization, providing a deeper understanding of Azure's capabilities as an infrastructure service. Each chapter includes online videos that visualize and enhance the concepts presented in the book, and access to a Windows app that provides instant Azure updates and demonstrates the process of going from on-premises to public cloud via Azure. Coverage includes storage customization, connectivity, virtual networks, backing up, hybrid environments, System Center management, and more, giving you everything you need to understand, evaluate, deploy, and maintain environments that utilize Microsoft Azure. Understand cost, options, and applications of Infrastructure as a Service (IaaS) Enable on- and off-premises connectivity to Azure Customize Azure templates and management processes Exploit key technologies and embrace the hybrid environment Mastering Microsoft Azure Infrastructure Services is your total solution.
