Microsoft Azure Machine Learning

Author: Sumit Mund

Publisher: Packt Publishing Ltd

ISBN: 1784398519

Category: Computers

Page: 212

View: 6931

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.
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Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition

Author: Valentine Fontama,Roger Barga,Wee Hyong Tok

Publisher: Apress

ISBN: 1484212002

Category: Computers

Page: 291

View: 6677

Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models. The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services. Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft. What’s New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration – a new feature announced February 2015 Data preparation and feature selection Data visualization with Power BI Recommendation engines Selling your models on Azure Marketplace
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Microsoft Azure Essentials Azure Machine Learning

Author: Jeff Barnes

Publisher: Microsoft Press

ISBN: 073569818X

Category: Computers

Page: 236

View: 5252

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.
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Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning

Author: Ginger Grant,Julio Granados,Guillermo Fernández,Pau Sempere,Javier Torrenteras,Paco Gonzalez,Tamanaco Francísquez

Publisher: Microsoft Press

ISBN: 013484968X

Category: Computers

Page: 336

View: 4771

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
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Azure Machine Learning Studio for the Non-data Scientist

Author: Michael Washington

Publisher: Createspace Independent Publishing Platform

ISBN: 9781548871123

Category:

Page: 158

View: 1521

Creating predictive models is no longer relegated to data scientists when you use tools such as the Microsoft Azure Machine Learning Studio. Azure Machine Learning Studio is a web browser-based application that allows you to create and deploy predictive models as web services that can be consumed by custom applications and other tools such as Microsoft Excel. With this book, you will learn how to create predictive experiments, operationalize them using Excel and Angular .Net Core applications, and create retraining programs to improve predictive results. Table of Contents Chapter 1: The Author is Not a Data Scientist * Why Do We Need Predictive Modeling? * An Introduction to Get You Started Chapter 2: An End-To-End Azure Machine Learning Studio Application * Create an Azure Machine Learning Workspace * Create An Experiment * Select Columns * Split Data * Train The Model * Score The Model * Evaluate The Model * Create A Predictive Web Service * Consume The Model Using Excel Chapter 3: An Angular 2 .Net Core Application Consuming an Azure Machine Learning Model * The Application * Creating The Application * Create The .Net Core Application * Add PrimeNG * Add The Database * Create Code To Call Azure Machine Learning Web Service * Create The Angular Application * Saving Data * Viewing Data Chapter 4: Retraining an Azure Machine Learning Application * The Retraining Process * Prepare The Training Data * Set-up An Azure Storage Account * Create The Batch Retraining Program * Get Required Values * Add A New Endpoint And Patch It * Consume The New Endpoint
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Deep Learning with Azure

Building and Deploying Artificial Intelligence Solutions on the Microsoft AI Platform

Author: Mathew Salvaris,Danielle Dean,Wee Hyong Tok

Publisher: Apress

ISBN: 1484236793

Category: Computers

Page: 284

View: 1163

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.
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Mengenal Microsoft Azure ML

Author: Agus Kurniawan

Publisher: Elex Media Komputindo

ISBN: 6020289745

Category: Computers

Page: 117

View: 3311

Era ledakan data sudah dimulai. Dengan meningkatnya data ini, kebutuhan akan analisis terhadap data ini juga akan meningkat. Machine Learning digunakan untuk memperoleh insight dari kumpulan data. Buku ini didesain dan dirancang untuk membantu para profesional dalam membangun aplikasi berbasis Microsoft Azure dan Machine Learning. Topik bahasan yang dijelaskan dalam buku ini adalah: • Mengenal Azure Machine Learning • Memprogram Machine Learning • Bekerja dengan Azure Machine Learning • Hands-On-Lab Azure Machine Learning • Kustomisasi Azure Machine Learning dengan R dan Python
Release

Datenanalyse mit Python

Auswertung von Daten mit Pandas, NumPy und IPython

Author: Wes McKinney

Publisher: O'Reilly

ISBN: 3960102143

Category: Computers

Page: 542

View: 1539

Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.6, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy, IPython und Jupyter kennen.Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehöriges Material des Buchs sind auf GitHub verfügbar.Aus dem Inhalt:Nutzen Sie die IPython-Shell und Jupyter Notebook für das explorative ComputingLernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennenSetzen Sie die Datenanalyse-Tools der pandasBibliothek einVerwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von DatenErstellen Sie interformative Visualisierungen mit matplotlibWenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassenAnalysieren und manipulieren Sie verschiedenste Zeitreihen-DatenFür diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einführung in statsmodels und scikit-learn.
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Microsoft Azure

Planning, Deploying, and Managing Your Data Center in the Cloud

Author: Marshall Copeland,Julian Soh,Anthony Puca,Mike Manning,David Gollob

Publisher: Apress

ISBN: 1484210433

Category: Computers

Page: 426

View: 3518

Written for IT and business professionals, this book provides the technical and business insight needed to plan, deploy and manage the services provided by the Microsoft Azure cloud. Find out how to integrate the infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) models with your existing business infrastructure while maximizing availability, ensuring continuity and safety of your data, and keeping costs to a minimum. The book starts with an introduction to Microsoft Azure and how it differs from Office 365—Microsoft’s ‘other’ cloud. You'll also get a useful overview of the services available. Part II then takes you through setting up your Azure account, and gets you up-and-running on some of the core Azure services, including creating web sites and virtual machines, and choosing between fully cloud-based and hybrid storage solutions, depending on your needs. Part III now takes an in-depth look at how to integrate Azure with your existing infrastructure. The authors, Anthony Puca, Mike Manning, Brent Rush, Marshall Copeland and Julian Soh, bring their depth of experience in cloud technology and customer support to guide you through the whole process, through each layer of your infrastructure from networking to operations. High availability and disaster recovery are the topics on everyone’s minds when considering a move to the cloud, and this book provides key insights and step-by-step guidance to help you set up and manage your resources correctly to optimize for these scenarios. You’ll also get expert advice on migrating your existing VMs to Azure using InMage, mail-in and the best 3rd party tools available, helping you ensure continuity of service with minimum disruption to the business. In the book’s final chapters, you’ll find cutting edge examples of cloud technology in action, from machine learning to business intelligence, for a taste of some exciting ways your business could benefit from your new Microsoft Azure deployment.
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Seri Belajar Data Science: Pengenalan Azure Machine Learning Studio

Author: M Reza Faisal,Erick Kurniawan

Publisher: M Reza Faisal

ISBN: N.A

Category:

Page: 167

View: 8078

**Cara Pembelian** Bagi yang tidak punya kartu kredit, maka pembelian dapat dilakukan dengan potong pulsa jika transaksi dilakukan pada device Android. Buku ini ditujukan bagi pembaca yang telah mengetahui konsep atau teori dari teknik, metode dan algoritma di bidang statistik dan machine learning, dan bagi pembaca yang ingin mencari tool yang dapat memudahkan menggunakan dan menerapkan konsep dan teori tersebut. Microsoft Azure ML Studio adalah tool berupa layanan komputasi awan yang berfungsi untuk membantu mengolah dan mengalisis data dengan berbagai metode konversi dan transformasi data, berbagai fungsi statistik serta bermacam-macam algoritma machine learning. Layanan seperti ini cocok digunakan bagi siapa saja yang bergelut di bidang data science namun tidak memiliki komputer dengan kinerja yang bagus. Atau kendala sumber daya listrik tidak selalu ada setiap waktu sehingga dapat mengganggu atau menghentikan pemrosesan data yang sedang berjalan. Maka dengan adanya layanan seperti Microsoft Azure ML Studio ini akan sangat membantu bagi siapa saja yang memiliki kendala serupa. Buku ini dibuat sebagai rangkuman dan catatan dari hal-hal yang penulis kerjakan dalam melakukan analisis dan pemrosesan data dengan Microsoft Azure ML Studio. Setiap pembahasan yang ditulis akan diberikan penjelesan sederhana tentang langkah-langkah yang dilakukan. Sehingga pembaca dapat mencoba langsung menyelesaikan masalah-masalah umum yang bidang statistik dan machine learning. **Isi Buku** 1 Pendahuluan - Komputasi Awan - Jenis-Jenis Layanan Komputasi Awan Infrastructure as a Service (IaaS) Platform as a Service Software as a Service - Microsoft Azure Program Gratis Mencoba Microsoft Azure Registrasi Portal Virtual Machine - Microsoft Azure Machine Learning Studio 2 Pengantar Azure ML Studio - Antarmuka Utama Projects Experiments Web services Notebooks Datasets Trained models Settings - Mengelola Dataset Menambah Dataset Melihat Dataset Menghapus Dataset - Mengelola Experiment Membuat Experiment Menjalankan Experiment Menyimpan Experiment Menghapus Experiment - Mengelola Modul Port Input & Output Bantuan & Dokumentasi Memberi Deskripsi Memberi Parameter Menghapus Modul 3 Data - Input Data Enter Data Manually Import Data - Missing Value Summarize Data Clean Missing Value - Duplicate Row Memilih & Mengabung Data Select Column in Dataset Add Columns Add Rows - Normalisasi Data Normalize Data - Sampling & Membagi Data Split Data Partition and Sample - Konversi Data 4 Fungsi Statistik - Operasi Matematika - Statistik Dasar - Korelasi Antar Variable - Distribusi Probabilitas - Hipotesis dengan t-Test 5 Machine Learning - Klasifikasi Klasifikasi Dua Class - Split Data Klasifikasi Dua Class - Cross Validation Klasifikasi Multi Class - Regresi Regresi - Split Data Regresi - Cross Validation - Clustering Sumber Data Clustering Hasil 6 Web Service Untuk Prediksi - Web Service - Setup Web Service Penentuan Experiment Membuat Web Service - Akses Web Service Akses dari Website Azure ML Akses dari Aplikasi Client 7 Topik Lanjutan - Modul dengan Bahasa Pemrograman R Versi R Input & Ouput Dataset Output R Device R Package Contoh Kasus - Klasifikasi Data Text dari Twitter Import Data Execute R Script Edit Metadata Feature Hashing Split Data Filter Based Feature Selection Train Model & Two-Class Support Vector Machine Score Model & Evaluate Model - Aplikasi Client Untuk Akses Azure ML Web Service Aplikasi Web Aplikasi Desktop Aplikasi Mobile Source Code 8 Penutup **Source Code & Free Ebook** Terima kasih bagi Anda mau membeli ebook ini. Ebook ini juga tersedia gratis jika Anda belum ingin membeli buku ini sekarang. Ebook gratis dapat diakses di link berikut: https://www.researchgate.net/publication/330184412_Seri_Belajar_Data_Science_Pengenalan_Azure_Machine_Learning_Studio Sedangkan source code contoh kasus paa ebook ini dapat diakses pada link berikut: https://github.com/rezafaisal/AzureMLStudioCodeSamples
Release

R in a Nutshell

Author: Joseph Adler

Publisher: O'Reilly Germany

ISBN: 3897216507

Category: Computers

Page: 768

View: 9617

Wozu sollte man R lernen? Da gibt es viele Gründe: Weil man damit natürlich ganz andere Möglichkeiten hat als mit einer Tabellenkalkulation wie Excel, aber auch mehr Spielraum als mit gängiger Statistiksoftware wie SPSS und SAS. Anders als bei diesen Programmen hat man nämlich direkten Zugriff auf dieselbe, vollwertige Programmiersprache, mit der die fertigen Analyse- und Visualisierungsmethoden realisiert sind – so lassen sich nahtlos eigene Algorithmen integrieren und komplexe Arbeitsabläufe realisieren. Und nicht zuletzt, weil R offen gegenüber beliebigen Datenquellen ist, von der einfachen Textdatei über binäre Fremdformate bis hin zu den ganz großen relationalen Datenbanken. Zudem ist R Open Source und erobert momentan von der universitären Welt aus die professionelle Statistik. R kann viel. Und Sie können viel mit R machen – wenn Sie wissen, wie es geht. Willkommen in der R-Welt: Installieren Sie R und stöbern Sie in Ihrem gut bestückten Werkzeugkasten: Sie haben eine Konsole und eine grafische Benutzeroberfläche, unzählige vordefinierte Analyse- und Visualisierungsoperationen – und Pakete, Pakete, Pakete. Für quasi jeden statistischen Anwendungsbereich können Sie sich aus dem reichen Schatz der R-Community bedienen. Sprechen Sie R! Sie müssen Syntax und Grammatik von R nicht lernen – wie im Auslandsurlaub kommen Sie auch hier gut mit ein paar aufgeschnappten Brocken aus. Aber es lohnt sich: Wenn Sie wissen, was es mit R-Objekten auf sich hat, wie Sie eigene Funktionen schreiben und Ihre eigenen Pakete schnüren, sind Sie bei der Analyse Ihrer Daten noch flexibler und effektiver. Datenanalyse und Statistik in der Praxis: Anhand unzähliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik lernen Sie, wie Sie Daten aufbereiten, mithilfe der Grafikfunktionen des lattice-Pakets darstellen, statistische Tests durchführen und Modelle anpassen. Danach werden Ihnen Ihre Daten nichts mehr verheimlichen.
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Industrie 4.0 im internationalen Kontext

Kernkonzepte, Ergebnisse, Trends

Author: Ronald Heinze,Christian Manzei,Linus Schleupner

Publisher: Beuth Verlag

ISBN: 3410260501

Category: Technology & Engineering

Page: 262

View: 4405

Nach Mechanisierung, Massenfertigung und Automatisierung kommt mit Industrie 4.0 jetzt die Digitalisierung. In dieser Publikation stellen namhafte Autoren die wichtigsten Bestandteile und wesentlichen Aspekte dieses übergreifenden Konzepts zur "Informatisierung der Wertschöpfungskette" vor. Stichpunkte aus dem Inhalt: Kernkonzepte und Basistechnologien // Standardisierungspfade // Internationale Konsortien und andere Initiativen (z. B. it's OWL) // Praxisberichte // Rechtliche Aspekte // Safety und Security // Ausbildung und Arbeitswelt // Analyse des derzeitigen Stellenwerts von Industrie 4.0 in der Praxis. Damit erschließt das Buch dem Leser die Potenziale, die sich aus der massiven Nutzung des Internets, der Integration von technischen Prozessen und Geschäftsprozessen, der digitalen Abbildung und Virtualisierung der realen Welt und der Möglichkeit "intelligenter" Produkte ergeben.
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Hands-On Machine Learning with Azure

Build powerful models with cognitive machine learning and artificial intelligence

Author: Thomas K Abraham,Parashar Shah,Jen Stirrup,Lauri Lehman,Anindita Basak

Publisher: Packt Publishing Ltd

ISBN: 1789130271

Category: Computers

Page: 340

View: 6534

Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies Key Features Learn advanced concepts in Azure ML and the Cortana Intelligence Suite architecture Explore ML Server using SQL Server and HDInsight capabilities Implement various tools in Azure to build and deploy machine learning models Book 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 learn Discover the benefits of leveraging the cloud for ML and AI Use Cognitive Services APIs to build intelligent bots Build a model using canned algorithms from Microsoft and deploy it as a web service Deploy virtual machines in AI development scenarios Apply R, Python, SQL Server, and Spark in Azure Build and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlow Implement model retraining in IoT, Streaming, and Blockchain solutions Explore best practices for integrating ML and AI functions with ADLA and logic apps Who 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
Release

SharePoint Kompendium -

Author: Marc André Zhou,Rainer Stropek,Benjamin Lanzendörfer

Publisher: N.A

ISBN: 3868027963

Category: Computers

Page: 64

View: 2102

Das SharePoint Kompendium ist ein Sammelband unterschiedlicher Artikel rund um das Thema SharePoint. Dieser Band gibt lesenswerte Inputs, wie die DSGVO Entwicklerteams zu neuen Höchstleistungen anspornen kann und wie mit Office 365 der Digitalisierung in globalen Teams begegnet werden kann. Des Weiteren gibt es einen tiefreichenden Einblick in das SharePoint Framework und wissenswerte Tipps, wie Microsoft Flow Ihnen hilft, Ihre Arbeit produktiver zu gestalten.
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Business in Real-Time Using Azure IoT and Cortana Intelligence Suite

Driving Your Digital Transformation

Author: Bob Familiar,Jeff Barnes

Publisher: Apress

ISBN: 148422650X

Category: Computers

Page: 525

View: 1066

Learn how today’s businesses can transform themselves by leveraging real-time data and advanced machine learning analytics. This book provides prescriptive guidance for architects and developers on the design and development of modern Internet of Things (IoT) and Advanced Analytics solutions. In addition, Business in Real-Time Using Azure IoT and Cortana Intelligence Suite offers patterns and practices for those looking to engage their customers and partners through Software-as-a-Service solutions that work on any device. Whether you're working in Health & Life Sciences, Manufacturing, Retail, Smart Cities and Buildings or Process Control, there exists a common platform from which you can create your targeted vertical solutions. Business in Real-Time Using Azure IoT and Cortana Intelligence Suite uses a reference architecture as a road map. Building on Azure’s PaaS services, you'll see how a solution architecture unfolds that demonstrates a complete end-to-end IoT and Advanced Analytics scenario. What You'll Learn: Automate your software product life cycle using PowerShell, Azure Resource Manager Templates, and Visual Studio Team Services Implement smart devices using Node.JS and C# Use Azure Streaming Analytics to ingest millions of events Provide both "Hot" and "Cold" path outputs for real-time alerts, data transformations, and aggregation analytics Implement batch processing using Azure Data Factory Create a new form of Actionable Intelligence (AI) to drive mission critical business processes Provide rich Data Visualizations across a wide variety of mobile and web devices Who This Book is For: Solution Architects, Software Developers, Data Architects, Data Scientists, and CIO/CTA Technical Leadership Professionals
Release

Machine Learning mit Python

Das Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning

Author: Sebastian Raschka

Publisher: MITP-Verlags GmbH & Co. KG

ISBN: 3958454240

Category: Computers

Page: 424

View: 6576

Release

Introducing Microsoft Azure HDInsight

Author: Avkash Chauhan,Valentine Fontama,Michele Hart,Wee-Hyong Tok,Buck Woody

Publisher: Microsoft Press

ISBN: 0133965910

Category: Computers

Page: 94

View: 1622

Microsoft Azure HDInsight is Microsoft’s 100 percent compliant distribution of Apache Hadoop on Microsoft Azure. This means that standard Hadoop concepts and technologies apply, so learning the Hadoop stack helps you learn the HDInsight service. At the time of this writing, HDInsight (version 3.0) uses Hadoop version 2.2 and Hortonworks Data Platform 2.0. In Introducing Microsoft Azure HDInsight, we cover what big data really means, how you can use it to your advantage in your company or organization, and one of the services you can use to do that quickly–specifically, Microsoft’s HDInsight service. We start with an overview of big data and Hadoop, but we don’t emphasize only concepts in this book–we want you to jump in and get your hands dirty working with HDInsight in a practical way. To help you learn and even implement HDInsight right away, we focus on a specific use case that applies to almost any organization and demonstrate a process that you can follow along with. We also help you learn more. In the last chapter, we look ahead at the future of HDInsight and give you recommendations for self-learning so that you can dive deeper into important concepts and round out your education on working with big data.
Release

Hit Refresh

Wie Microsoft sich neu erfunden hat und die Zukunft verändert

Author: Satya Nadella,Jill Tracie Nichols,Greg Shaw

Publisher: Plassen Verlag

ISBN: 3864704847

Category: Political Science

Page: 256

View: 6778

Lange bevor es iPad oder Kindle gab, hatte Microsoft bereits ein Tablet und einen E-Reader entwickelt. Nur: Es hatte die Produkte nie genutzt und war gegen neue Innovatoren wie Apple, Google und Amazon ins Hintertreffen geraten. Um diesen Rückstand wieder aufzuholen, musste Microsoft nicht nur eine Strategie entwickeln, wie es künftig mit neuen Technologien wie künstlicher Intelligenz, Robotern und Co umgehen wollte – der einstige Vorreiter in Sachen Innovation musste seine Kultur vollkommen erneuern und seine Seele wiederfinden. Dies ist die Inside-Story dieser Transformation.
Release

Machine Learning with Microsoft Technologies

Selecting the Right Architecture and Tools for Your Project

Author: Leila Etaati

Publisher: Apress

ISBN: 9781484236574

Category: Computers

Page: 350

View: 5068

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 solution Create and manage Microsoft’s tool environments for development, testing, and production of a machine learning project Implement 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.
Release

Stream Analytics with Microsoft Azure

Real-time data processing for quick insights using Azure Stream Analytics

Author: Anindita Basak,Krishna Venkataraman,Ryan Murphy,Manpreet Singh

Publisher: Packt Publishing Ltd

ISBN: 1788390628

Category: Computers

Page: 286

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Develop and manage effective real-time streaming solutions by leveraging the power of Microsoft Azure About This Book Analyze your data from various sources using Microsoft Azure Stream Analytics Develop, manage and automate your stream analytics solution with Microsoft Azure A practical guide to real-time event processing and performing analytics on the cloud Who This Book Is For If you are looking for a resource that teaches you how to process continuous streams of data in real-time, this book is what you need. A basic understanding of the concepts in analytics is all you need to get started with this book What You Will Learn Perform real-time event processing with Azure Stream Analysis Incorporate the features of Big Data Lambda architecture pattern in real-time data processing Design a streaming pipeline for storage and batch analysis Implement data transformation and computation activities over stream of events Automate your streaming pipeline using Powershell and the .NET SDK Integrate your streaming pipeline with popular Machine Learning and Predictive Analytics modelling algorithms Monitor and troubleshoot your Azure Streaming jobs effectively In Detail Microsoft Azure is a very popular cloud computing service used by many organizations around the world. Its latest analytics offering, Stream Analytics, allows you to process and get actionable insights from different kinds of data in real-time. This book is your guide to understanding the basics of how Azure Stream Analytics works, and building your own analytics solution using its capabilities. You will start with understanding what Stream Analytics is, and why it is a popular choice for getting real-time insights from data. Then, you will be introduced to Azure Stream Analytics, and see how you can use the tools and functions in Azure to develop your own Streaming Analytics. Over the course of the book, you will be given comparative analytic guidance on using Azure Streaming with other Microsoft Data Platform resources such as Big Data Lambda Architecture integration for real time data analysis and differences of scenarios for architecture designing with Azure HDInsight Hadoop clusters with Storm or Stream Analytics. The book also shows you how you can manage, monitor, and scale your solution for optimal performance. By the end of this book, you will be well-versed in using Azure Stream Analytics to develop an efficient analytics solution that can work with any type of data. Style and approach A comprehensive guidance on developing real-time event processing with Azure Stream Analysis
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