Predictive Analytics with Microsoft Azure Machine Learning, Second Edition

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

About the Authors

Roger Barga is a General Manager and Director of Development at Amazon Web Services. Prior to joining Amazon, Roger was Group Program Manager for the Cloud Machine Learning group in the Cloud & Enterprise division at Microsoft, where his team was responsible for product management of the Azure Machine Learning service. Roger joined Microsoft in 1997 as a Researcher in the Database Group of Microsoft Research, where he directed both systems research and product development efforts in database, workflow, and stream processing systems. He has developed ideas from basic research, through proof of concept prototypes, to incubation efforts in product groups. Prior to joining Microsoft, Roger was a Research Scientist in the Machine Learning Group at the Pacific Northwest National Laboratory where he built and deployed machine learning-based solutions. Roger is also an Affiliate Professor at the University of Washington, where he is a lecturer in the Data Science and Machine Learning programs.

Roger holds a PhD in Computer Science, a M.Sc. in Computer Science with an emphasis on Machine Learning, and a B.Sc. in Mathematics and Computing Science. He has published over 90 peer-reviewed technical papers and book chapters, collaborated with 214 co-authors from 1991 to 2013, with over 700 citations by 1,084 authors.

Valentine Fontama is a Principal Data Scientist in the Data and Decision Sciences Group (DDSG) at Microsoft, where he leads external consulting engagements that deliver world-class Advanced Analytics solutions to Microsoft’s customers. Val has over 18 years of experience in data science and business. Following a PhD in Artificial Neural Networks, he applied data mining in the environmental science and credit industries. Before Microsoft, Val was a New Technology Consultant at Equifax in London where he pioneered the application of data mining to risk assessment and marketing in the consumer credit industry. He is currently an Affiliate Professor of Data Science at the University of Washington. In his prior role at Microsoft, Val was a Senior Product Marketing Manager responsible for big data and predictive analytics in cloud and enterprise marketing. In this role, he led product management for Microsoft Azure Machine Learning; HDInsight, the first Hadoop service from Microsoft; Parallel Data Warehouse, Microsoft’s first data warehouse appliance; and three releases of Fast Track Data Warehouse. He also played a key role in defining Microsoft’s strategy and positioning for in-memory computing. Val holds an M.B.A. in Strategic Management and Marketing from Wharton Business School, a Ph.D. in Neural Networks, a M.Sc. in Computing, and a B.Sc. in Mathematics and Electronics (with First Class Honors). He co-authored the book Introducing Microsoft Azure HDInsight, and has published 11 academic papers with 152 citations by over 227 authors.

Wee-Hyong Tok is a Senior Program Manager of the Information Management and Machine Learning (IMML) team in the Cloud and Enterprise group at Microsoft Corp. Wee-Hyong brings decades of database systems experience, spanning industry and academia.

Prior to pursuing his PhD, Wee-Hyong was a System Analyst at a large telecommunication company in Singapore. Wee-Hyong was a SQL Server Most Valuable Professional (MVP), specializing in business intelligence and data mining. He was responsible for spearheading data mining boot camps in Southeast Asia, with a goal of empowering IT professionals with the knowledge and skills to use analytics in their organization to turn raw data into insights.

He joined Microsoft and worked on the SQL Server team, and is responsible for shaping the SSIS Server, bringing it from concept to release in SQL Server 2012.

Wee Hyong holds a Ph.D. in Computer Science, M.Sc. in Computing, and a B.Sc. (First Class Honors) in Computer Science, from the National University of Singapore. He has published 21 peer reviewed academic papers and journals. He is a co-author of the following books: Predictive Analytics with Microsoft Azure Machine Learning, Introducing Microsoft Azure HDInsight, and Microsoft SQL Server 2012 Integration Services.