Microsoft Azure Machine Learning

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Microsoft Azure Essentials Azure Machine Learning

Author : Jeff Barnes
Publisher : Microsoft Press
ISBN 13 : 073569818X
Total Pages : 393 pages
Book Rating : 4.7/5 (356 download)

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Book Synopsis Microsoft Azure Essentials Azure Machine Learning by : Jeff Barnes

Download or read book Microsoft Azure Essentials Azure Machine Learning written by Jeff Barnes and published by Microsoft Press. This book was released on 2015-04-25 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Microsoft Azure Machine Learning

Author : Sumit Mund
Publisher : Packt Publishing Ltd
ISBN 13 : 1784398519
Total Pages : 212 pages
Book Rating : 4.7/5 (843 download)

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Book Synopsis Microsoft Azure Machine Learning by : Sumit Mund

Download or read book Microsoft Azure Machine Learning written by Sumit Mund and published by Packt Publishing Ltd. This book was released on 2015-06-16 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides you with the skills necessary to get started with Azure Machine Learning to build predictive models as quickly as possible, in a very intuitive way, whether you are completely new to predictive analysis or an existing practitioner. The book starts by exploring ML Studio, the browser-based development environment, and explores the first step—data exploration and visualization. You will then build different predictive models using both supervised and unsupervised algorithms, including a simple recommender system. The focus then shifts to learning how to deploy a model to production and publishing it as an API. The book ends with a couple of case studies using all the concepts and skills you have learned throughout the book to solve real-world problems.

Hands-On Machine Learning with Azure

Author : Thomas K Abraham
Publisher : Packt Publishing Ltd
ISBN 13 : 1789130271
Total Pages : 331 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis Hands-On Machine Learning with Azure by : Thomas K Abraham

Download or read book Hands-On Machine Learning with Azure written by Thomas K Abraham and published by Packt Publishing Ltd. This book was released on 2018-10-31 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Predictive Analytics with Microsoft Azure Machine Learning

Author : Valentine Fontama
Publisher : Apress
ISBN 13 : 148420445X
Total Pages : 178 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Predictive Analytics with Microsoft Azure Machine Learning by : Valentine Fontama

Download or read book Predictive Analytics with Microsoft Azure Machine Learning written by Valentine Fontama and published by Apress. This book was released on 2014-11-25 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis. The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter. The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft.

Automated Machine Learning with Microsoft Azure

Author : Dennis Michael Sawyers
Publisher : Packt Publishing Ltd
ISBN 13 : 1800561970
Total Pages : 340 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Automated Machine Learning with Microsoft Azure by : Dennis Michael Sawyers

Download or read book Automated Machine Learning with Microsoft Azure written by Dennis Michael Sawyers and published by Packt Publishing Ltd. This book was released on 2021-04-23 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning with Microsoft Technologies

Author : Leila Etaati
Publisher : Apress
ISBN 13 : 1484236580
Total Pages : 363 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Machine Learning with Microsoft Technologies by : Leila Etaati

Download or read book Machine Learning with Microsoft Technologies written by Leila Etaati and published by Apress. This book was released on 2019-06-12 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Mastering Azure Machine Learning

Author : Kaijisse Waaijer
Publisher :
ISBN 13 : 9781789807554
Total Pages : 394 pages
Book Rating : 4.8/5 (75 download)

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Book Synopsis Mastering Azure Machine Learning by : Kaijisse Waaijer

Download or read book Mastering Azure Machine Learning written by Kaijisse Waaijer and published by . This book was released on 2020-04-30 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.

Introducing Machine Learning

Author : Dino Esposito
Publisher : Microsoft Press
ISBN 13 : 0135588383
Total Pages : 617 pages
Book Rating : 4.1/5 (355 download)

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Book Synopsis Introducing Machine Learning by : Dino Esposito

Download or read book Introducing Machine Learning written by Dino Esposito and published by Microsoft Press. This book was released on 2020-01-31 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Practical Automated Machine Learning on Azure

Author : Deepak Mukunthu
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492055549
Total Pages : 198 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Practical Automated Machine Learning on Azure by : Deepak Mukunthu

Download or read book Practical Automated Machine Learning on Azure written by Deepak Mukunthu and published by "O'Reilly Media, Inc.". This book was released on 2019-09-23 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Exam Ref AI-900 Microsoft Azure AI Fundamentals

Author : Julian Sharp
Publisher : Microsoft Press
ISBN 13 : 9780137358038
Total Pages : 0 pages
Book Rating : 4.3/5 (58 download)

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Book Synopsis Exam Ref AI-900 Microsoft Azure AI Fundamentals by : Julian Sharp

Download or read book Exam Ref AI-900 Microsoft Azure AI Fundamentals written by Julian Sharp and published by Microsoft Press. This book was released on 2021-11-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Direct from Microsoft, this Exam Ref is the official study guide for the new Microsoft AI-900 Microsoft Azure AI Fundamentals certification exam. Exam Ref AI-900 Microsoft Azure AI Fundamentals offers professional-level preparation that helps candidates maximize their exam performance and sharpen their skills on the job. It focuses on the specific areas of expertise modern IT professionals need to demonstrate real-world mastery of common machine learning (ML) and artificial intelligence (AI) workloads and how to use them in Azure.

Deep Learning with Azure

Author : Mathew Salvaris
Publisher : Apress
ISBN 13 : 1484236793
Total Pages : 298 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Deep Learning with Azure by : Mathew Salvaris

Download or read book Deep Learning with Azure written by Mathew Salvaris and published by Apress. This book was released on 2018-08-24 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning for Time Series Forecasting with Python

Author : Francesca Lazzeri
Publisher : John Wiley & Sons
ISBN 13 : 111968238X
Total Pages : 224 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Machine Learning for Time Series Forecasting with Python by : Francesca Lazzeri

Download or read book Machine Learning for Time Series Forecasting with Python written by Francesca Lazzeri and published by John Wiley & Sons. This book was released on 2020-12-03 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

Azure Data Scientist Associate Certification Guide

Author : Andreas Botsikas
Publisher : Packt Publishing Ltd
ISBN 13 : 1800561261
Total Pages : 448 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis Azure Data Scientist Associate Certification Guide by : Andreas Botsikas

Download or read book Azure Data Scientist Associate Certification Guide written by Andreas Botsikas and published by Packt Publishing Ltd. This book was released on 2021-12-03 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Microsoft Azure Essentials - Fundamentals of Azure

Author : Michael Collier
Publisher : Microsoft Press
ISBN 13 : 0735697302
Total Pages : 400 pages
Book Rating : 4.7/5 (356 download)

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Book Synopsis Microsoft Azure Essentials - Fundamentals of Azure by : Michael Collier

Download or read book Microsoft Azure Essentials - Fundamentals of Azure written by Michael Collier and published by Microsoft Press. This book was released on 2015-01-29 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 Machine Learning Engineering

Author : Sina Fakhraee
Publisher : Packt Publishing Ltd
ISBN 13 : 1803241683
Total Pages : 362 pages
Book Rating : 4.8/5 (32 download)

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Book Synopsis Azure Machine Learning Engineering by : Sina Fakhraee

Download or read book Azure Machine Learning Engineering written by Sina Fakhraee and published by Packt Publishing Ltd. This book was released on 2023-01-20 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service Key FeaturesAutomate complete machine learning solutions using Microsoft AzureUnderstand how to productionize machine learning modelsGet to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learningBook Description Data scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. You'll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide. Throughout the book, you'll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. You'll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework. By the end of this Azure Machine Learning book, you'll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios. What you will learnTrain ML models in the Azure Machine Learning serviceBuild end-to-end ML pipelinesHost ML models on real-time scoring endpointsMitigate bias in ML modelsGet the hang of using an MLOps framework to productionize modelsSimplify ML model explainability using the Azure Machine Learning service and Azure InterpretWho this book is for Machine learning engineers and data scientists who want to move to ML engineering roles will find this AMLS book useful. Familiarity with the Azure ecosystem will assist with understanding the concepts covered.

Programming ML.NET

Author : Dino Esposito
Publisher : Microsoft Press
ISBN 13 : 0137383622
Total Pages : 549 pages
Book Rating : 4.1/5 (373 download)

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Book Synopsis Programming ML.NET by : Dino Esposito

Download or read book Programming ML.NET written by Dino Esposito and published by Microsoft Press. This book was released on 2022-02-03 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The expert guide to creating production machine learning solutions with ML.NET! ML.NET brings the power of machine learning to all .NET developers— and Programming ML.NET helps you apply it in real production solutions. Modeled on Dino Esposito's best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsoft's team used to build ML.NET itself. After a foundational overview of ML.NET's libraries, the authors illuminate mini-frameworks (“ML Tasks”) for regression, classification, ranking, anomaly detection, and more. For each ML Task, they offer insights for overcoming common real-world challenges. Finally, going far beyond shallow learning, the authors thoroughly introduce ML.NET neural networking. They present a complete example application demonstrating advanced Microsoft Azure cognitive services and a handmade custom Keras network— showing how to leverage popular Python tools within .NET. 14-time Microsoft MVP Dino Esposito and son Francesco Esposito show how to: Build smarter machine learning solutions that are closer to your user's needs See how ML.NET instantiates the classic ML pipeline, and simplifies common scenarios such as sentiment analysis, fraud detection, and price prediction Implement data processing and training, and “productionize” machine learning–based software solutions Move from basic prediction to more complex tasks, including categorization, anomaly detection, recommendations, and image classification Perform both binary and multiclass classification Use clustering and unsupervised learning to organize data into homogeneous groups Spot outliers to detect suspicious behavior, fraud, failing equipment, or other issues Make the most of ML.NET's powerful, flexible forecasting capabilities Implement the related functions of ranking, recommendation, and collaborative filtering Quickly build image classification solutions with ML.NET transfer learning Move to deep learning when standard algorithms and shallow learning aren't enough “Buy” neural networking via the Azure Cognitive Services API, or explore building your own with Keras and TensorFlow