Hands-On Machine Learning with Azure

Build powerful models with cognitive machine learning and artificial intelligence

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

Packt(GCO Science) 2018년 10월 31일 출간

작품소개

책소개

▶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

▶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

▶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

목차

▶TABLE of CONTENTS
1: AI CLOUD FOUNDATIONS
2: DATA SCIENCE PROCESS
3: COGNITIVE SERVICES
4: BOT FRAMEWORK
5: AZURE MACHINE LEARNING STUDIO
6: SCALABLE COMPUTING FOR DATA SCIENCE
7: MACHINE LEARNING SERVER
8: HDINSIGHT
9: MACHINE LEARNING WITH SPARK
10: BUILDING DEEP LEARNING SOLUTIONS
11: INTEGRATION WITH OTHER AZURE SERVICES
12: END-TO-END MACHINE LEARNING

책 속으로

▶What this book covers
？ Chapter 1, AI Cloud Foundations, introduces readers to the Microsoft Azure cloud and the reasons for choosing it as a platform for AI projects. We also describe the important services available to users looking to build AI solutions. This chapter also describes a decision flowchart to help pick and choose the right services on Azure that fit the business needs of an AI project.

？ Chapter 2, Data Science Process, focuses on the frameworks available for data science projects in a structured and organized manner. We will look at the principles of Team Data Science Process (TDSP) and the utilities available to support it. This chapter goes into the details of each step and helps define the criteria for success at every stage of the process.

？ Chapter 3, Cognitive Services, covers Cognitive Services in Azure, which makes it quick and simple to build smart applications. We will take a deep dive at some of the API that can be used to build AI applications without being a machine learning expert.

？ Chapter 4, Bot Framework, explains how to build bots using bot-related services in Azure. We will go through these options in a step-by-step manner to help you get started quickly.

？ Chapter 5, Azure Machine Learning Studio, explores Azure Machine Learning Studio and its advantages, and shows how we can build experiments in Azure Machine Learning Studio.

？ Chapter 6, Scalable Computing for Data Science, covers the vertical and horizontal scaling options in Azure to leverage cloud computing.

？ Chapter 7, Machine Learning Server, explains what the Microsoft Machine Learning Server is and also looks at key parts of the R and Python architecture.

？ Chapter 8, HDInsight, covers various functions of HDInsight in R and how to use them.

？ Chapter 9, Machine Learning with Spark, explains how to use Azure HDInsight in Spark, and explains what machine learning with Azure Databricks is like.

？ Chapter 10, Building Deep Learning Solutions, executes the steps of the popular open source deep learning tool, TensorFlow, on an Azure deep learning VM, and also covers the features of Azure Notebooks. The chapter also highlights the utilization of other deep learning frameworks, such as Keras, Pytorch, Caffe, Theano, and Chainer, using AI tools for Visual Studio/VS code and specifies deeper insights.

？ Chapter 11, Integration with Other Azure Services, covers typical integration patterns with other non-AI services in Azure. The reader will gain a deeper understanding of the options and best practices for integrating with functions, ADLA, and logic apps in AI solutions.

？ Chapter 12, End-to-End Machine Learning, explains how to get started with Azure Machine Learning services for end-to-end custom machine learning.

출판사 서평

▶ Preface
This book will teach you how advanced machine learning can be performed in the cloud in a very cheap way. You will learn more about Azure Machine Learning processes as an enterprise-ready methodology. 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. It will help you to discover the different benefits of leveraging the cloud for machine learning and AI, deploy virtual machines in AI development scenarios, and how to apply R, Python, SQL Server, and Spark in Azure.

By the end of this book, you will be able to implement machine learning and AI concepts in your model to solve real-world problems.

작가정보

저자(글) Thomas K Abraham

Dr. Thomas K Abraham is a cloud solution architect (advanced analytics and AI) at Microsoft in the South Central Region of the USA. Since January 2016, he's been assisting organizations in leveraging technologies such as SQL, Spark, Hadoop, NoSQL, BI, and AI on Azure. Prior to that, Thomas spent 10 years in Ecolab, where he designed algorithms for IoT devices and built solutions for anomaly detection. In the oil and gas division, he designed and built customer-facing analytics solutions for multiple super majors. His work was focused on preventing equipment failure by modeling corrosion, scale, and other stresses. He has a PhD in Chemical Engineering from The Ohio State University in 2005. His thesis focused on the use of nonlinear optimization with reaction models.

저자(글) Parashar Shah

Parashar Shah is a Senior Program Manager in the Azure Machine Learning platform team.Currently, he works on making Azure Machine Learning services the best place to do e2e machine learning for building custom AI solutions using big data. Previously at Microsoft, he has been a Data Scientist and a Data Solutions Architect in various Cloud and AI teams.Prior to joining Microsoft, Parashar worked at Nokia Networks as a Solutions Architect & Product Manager building customer experience analytics solutions for global telcos. He also co-founded a carpooling startup, which helped employees carpool safely. He has 10+ years of global work experience. He is an alum of Indian Institute of Management, Bangalore and Gujarat University.

저자(글) Jen Stirrup

Jen Stirrup is a data strategist and technologist, a Microsoft Most Valuable Professional (MVP), and a Microsoft Regional Director, a tech community advocate, a public speaker and blogger, a published author, and a keynote speaker. Jen is the founder of a boutique consultancy based in the UK, Data Relish, which focuses on delivering successful business intelligence and artificial intelligence solutions that add real value to customers worldwide. She has featured on the BBC as a guest expert on topics relating to data.

저자(글) Lauri Lehman

Lauri Lehman is a data scientist who is focused on machine learning tools in Azure. He helps customers to design and implement machine learning solutions in the cloud. He works for the software consultancy company, Zure, based in Helsinki, Finland. For the past 4 years, Lauri has specialized in data and machine learning in Azure. He has worked on many machine learning projects, developing solutions for demand estimation, text analytics, and image recognition, for example. Lauri has previously worked as an academic researcher in theoretical physics, after obtaining his PhD on topological quantum walks. He still likes to follow the progress of modern physics and is eagerly a waiting the era of quantum machine learning!ㅍ

저자(글) Anindita Basak

Anindita Basak works as a cloud solution architect in data analytics and AI platforms and has been working with Microsoft Azure from its inception. With over a decade of experience, she helps enterprises to enable their digital transformation journey empowered with cloud, data, and AI. She has worked with various teams at Microsoft as FTE in the role of Azure Development Support Engineer, Pro-Direct Delivery Manager, and Technical Consultant. She recently co-authored the book Stream Analytics with Microsoft Azure, and was a technical reviewer for various technologies, including data-intensive applications, Azure HDInsigt, SQL Server BI, IoT, and Decision Science for Packt. She has also authored two video courses on Azure Stream Analytics from Packt.