Mastering Azure Machine Learning
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Mastering Azure Machine Learning
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Author : Christoph Körner
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-30
Mastering Azure Machine Learning written by Christoph Körner and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-30 with Computers categories.
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.
Mastering Azure Machine Learning
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Author : Christoph Korner
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-05-10
Mastering Azure Machine Learning written by Christoph Korner and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-10 with Computers categories.
Supercharge 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.
Mastering Azure Machine Learning
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Author : Christoph Korner
language : en
Publisher:
Release Date : 2020
Mastering Azure Machine Learning written by Christoph Korner and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
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 Features Make sense of data on the cloud by implementing advanced analytics Train and optimize advanced deep learning models efficiently on Spark using Azure Databricks Deploy 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 ML and takes you through the process of data experimentation, data preparation, and feature engineering using Azure ML 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 AutoML and HyperDrive, and perform distributed training on Azure ML. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure ML, 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 ML and be able to confidently design, build and operate scalable ML pipelines in Azure. What you will learn Setup your Azure ML workspace for data experimentation and visualization Perform ETL, data preparation, and feature extraction using Azure best practices Implement advanced feature extraction using NLP and word embeddings Train gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure ML Use hyperparameter tuning and AutoML to optimize your ML models Employ distributed ML on GPU clusters using Horovod in Azure ML Deploy, operate and manage your ML models at scale Automated your end-to-end ML process as CI/CD pipelines for MLOps Who this book is for This machine learning book is for data professionals, data analysts, data enginee...
Microsoft Azure Machine Learning
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Author : Sumit Mund
language : en
Publisher:
Release Date : 2015-06-16
Microsoft Azure Machine Learning written by Sumit Mund and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-16 with Computers categories.
The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.
Practical Automated Machine Learning On Azure
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Author : Deepak Mukunthu
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-09-23
Practical Automated Machine Learning On Azure written by Deepak Mukunthu and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-23 with Computers categories.
Develop 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.
Mastering Azure Machine Learning
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Author : Bimal Kujur
language : en
Publisher: Independently Published
Release Date : 2025-01-18
Mastering Azure Machine Learning written by Bimal Kujur and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-18 with Computers categories.
"Mastering Azure Machine Learning: From Fundamentals to Deployment" is designed to bridge the gap between foundational ML concepts and the complexities of deploying production-ready systems. While Azure ML's intuitive interface and robust APIs simplify many tasks, mastery of the platform requires an understanding of the underlying concepts, tools, and workflows. This book equips you with the skills and knowledge to navigate every stage of the machine learning lifecycle using Azure ML-from data preparation and model development to deployment, monitoring, and scaling. This book caters to a broad audience: Beginners: Individuals new to machine learning who want a structured guide to mastering Azure ML's features while understanding fundamental ML concepts. Intermediate Practitioners: Those with some experience in ML who wish to deepen their knowledge of Azure ML's advanced capabilities, such as AutoML, distributed training, and pipeline orchestration. Professionals: Industry experts seeking insights into implementing scalable and efficient ML solutions in real-world scenarios. Regardless of your starting point, this book emphasizes practical examples, clear explanations, and actionable insights to accelerate your Azure ML journey. What You Will Learn The book's modular structure ensures a comprehensive learning experience: Foundations: Introduction to Azure ML, its architecture, and core capabilities. Model Development: Hands-on guidance for building and training models, including data preprocessing, experimentation, and hyperparameter tuning. AutoML and Responsible AI: Insights into automation, interpretability, and fairness to ensure ethical and impactful solutions. Advanced Techniques: Distributed training, embedding models, and working with large language models. Deployment: Real-world approaches to deploying, monitoring, and scaling ML systems on Azure. Case Studies and Best Practices: Success stories across industries and actionable best practices for leveraging Azure ML effectively.
Microsoft Azure Essentials Azure Machine Learning
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Author : Jeff Barnes
language : en
Publisher: Microsoft Press
Release Date : 2015-04-25
Microsoft Azure Essentials Azure Machine Learning written by Jeff Barnes and has been published by Microsoft Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-25 with Computers categories.
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.
Mastering Azure Analytics
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Author : Zoiner Tejada
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-04-06
Mastering Azure Analytics written by Zoiner Tejada and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-06 with Computers categories.
Helps 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.
Automated Machine Learning With Microsoft Azure
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Author : Dennis Michael Sawyers
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-04-23
Automated Machine Learning With Microsoft Azure written by Dennis Michael Sawyers and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-23 with Computers categories.
A 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.
Mastering Identity And Access Management With Microsoft Azure
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Author : Jochen Nickel
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-09-30
Mastering Identity And Access Management With Microsoft Azure written by Jochen Nickel and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-30 with Computers categories.
Start empowering users and protecting corporate data, while managing Identities and Access with Microsoft Azure in different environments About This Book Deep dive into the Microsoft Identity and Access Management as a Service (IDaaS) solution Design, implement and manage simple and complex hybrid identity and access management environments Learn to apply solution architectures directly to your business needs and understand how to identify and manage business drivers during transitions Who This Book Is For This book is for business decision makers, IT consultants, and system and security engineers who wish to plan, design, and implement Identity and Access Management solutions with Microsoft Azure. What You Will Learn Apply technical descriptions and solution architectures directly to your business needs and deployments Identify and manage business drivers and architecture changes to transition between different scenarios Understand and configure all relevant Identity and Access Management key features and concepts Implement simple and complex directory integration, authentication, and authorization scenarios Get to know about modern identity management, authentication, and authorization protocols and standards Implement and configure a modern information protection solution Integrate and configure future improvements in authentication and authorization functionality of Windows 10 and Windows Server 2016 In Detail Microsoft Azure and its Identity and Access Management is at the heart of Microsoft's Software as a Service, including Office 365, Dynamics CRM, and Enterprise Mobility Management. It is an essential tool to master in order to effectively work with the Microsoft Cloud. Through practical, project based learning this book will impart that mastery. Beginning with the basics of features and licenses, this book quickly moves on to the user and group lifecycle required to design roles and administrative units for role-based access control (RBAC). Learn to design Azure AD to be an identity provider and provide flexible and secure access to SaaS applications. Get to grips with how to configure and manage users, groups, roles, and administrative units to provide a user- and group-based application and self-service access including the audit functionality. Next find out how to take advantage of managing common identities with the Microsoft Identity Manager 2016 and build cloud identities with the Azure AD Connect utility. Construct blueprints with different authentication scenarios including multi-factor authentication. Discover how to configure and manage the identity synchronization and federation environment along with multi -factor authentication, conditional access, and information protection scenarios to apply the required security functionality. Finally, get recommendations for planning and implementing a future-oriented and sustainable identity and access management strategy. Style and approach A practical, project-based learning experience explained through hands-on examples.