
Hands-On Machine Learning with Azure
Build powerful models with cognitive machine learning and artificial intelligence
Packt Publishing
Published on 31. October 2018
Book
Paperback/Softback
340 pages
978-1-78913-195-6 (ISBN)
Description
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 DescriptionImplementing 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 forIf 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
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 DescriptionImplementing 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 forIf 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
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 19 mm
Weight
636 gr
ISBN-13
978-1-78913-195-6 (9781789131956)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Thomas K. Abraham | Parashar Shah | Jen Stirrup
Hands-On Machine Learning with Azure
Build powerful models with cognitive machine learning and artificial intelligence
E-Book
09/2024
Packt Publishing
€34.99
Available for download
Persons
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. 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. 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. 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! Anindita Basak is a cloud architect with almost 15+ years of experience, the last 12 years of which she has been extensively working on Azure. She has delivered various real-time implementations on Azure data analytics, and cloud-native and real-time event-driven architecture for Fortune 500 enterprises, ranging from banking, financial services, and insurance (BFSI)to retail sectors. She is also a cloud and DataOps trainer and consultant, and author of cloud AI and DevOps books. Ryan Murphy, one of the key Co-authors of Stream Analytics with Microsoft Azure is a Solution Architect living in Saint Louis, Missouri. He has been building and innovating with data for nearly twenty years, including extensive work in the gaming and agriculture industries. Currently, Ryan is helping some of the worlds largest organizations modernize their business with data solutions powered by the Microsoft Azure cloud.
Content
Table of Contents
AI Cloud Foundations
Data Science Process
Cognitive Services
Bot Framework
Azure Machine Learning Studio
Scalable Computing for Data Science
Machine Learning Server
HDInsight
Machine Learning with Spark
Building Deep Learning Solutions
Integration with Other Azure Services
An End to End Example
AI Cloud Foundations
Data Science Process
Cognitive Services
Bot Framework
Azure Machine Learning Studio
Scalable Computing for Data Science
Machine Learning Server
HDInsight
Machine Learning with Spark
Building Deep Learning Solutions
Integration with Other Azure Services
An End to End Example