Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning Contributor(s): Lakshmanan, Valliappa (Author) |
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ISBN: 1491974567 ISBN-13: 9781491974568 Publisher: O'Reilly Media
Binding Type: Paperback - See All Available Formats & Editions Published: January 2018 * Out of Print * |
Additional Information |
BISAC Categories: - Computers | Data Modeling & Design - Computers | Databases - General |
Physical Information: 0.8" H x 7" W x 9.1" L (1.40 lbs) 404 pages |
Features: Price on Product |
Descriptions, Reviews, Etc. |
Publisher Description: Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by implementing these statistical and machine learning solutions in your own project on GCP, and discover how this platform provides a transformative and more collaborative way of doing data science. You'll learn how to:
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Contributor Bio(s): Lakshmanan, Valliappa: - Valliappa (Lak) Lakshmanan is currently a Tech Lead for Data and Machine Learning Professional Services for Google Cloud. His mission is to democratize machine learning so that it can be done by anyone anywhere using Google's amazing infrastructure, without deep knowledge of statistics or programming or ownership of a lot of hardware. Before Google, he led a team of data scientists at the Climate Corporation and was a Research Scientist at NOAA National Severe Storms Laboratory, working on machine learning applications for severe weather diagnosis and prediction. |
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