Hands-On Machine Learning with Azure: Build powerful models with cognitive machine learning and artificial intelligence

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

Overview

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.

Product Details

ISBN-13:	9781789130270
Publisher:	Packt Publishing
Publication date:	10/31/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	340
File size:	24 MB
Note:	This product may take a few minutes to download.

About the Author

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 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.

Table of Contents

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. An End to End Example