Data Science on the Google Cloud Platform: Implementing End-to-End Real-time Data Pipelines: from in 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Valliappa Lakshmanan 作者

O'Reilly Media

译者

2017-10-25 出版日期

250 页数

USD 34.80 价格

Paperback

丛书系列

9781491974568 图书编码

Data Science on the Google Cloud Platform: Implementing End-to-End Real-time Data Pipelines: from in 在线电子书图书标签: 机器学习计算机数据科学云计算 AI 大数据 machine learning

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About the Author

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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Data Science on the Google Cloud Platform: Implementing End-to-End Real-time Data Pipelines: from in 在线电子书图书描述

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). With this practical guide, author and GCP Program Manager Valliappa Lakshmanan shows you how to gain insight into a sample business decision by applying different statistical and machine learning methods and tools.Along the way, you’ll get an extensive tour of the big data and machine learning parts of GCP. You’ll start with statistical methods, move into straightforward classification, and then explore windowing and real-time prediction.Move from basic to increasingly sophisticated methodsUnderstand interactive querying of very large datasets with BigQueryLearn about probabilistic decision making with SparkSQL and SparkTrain a TensorFlow model in Python and call it from JavaCreate a data processing pipeline with DataflowCompute time-windowed aggregates in real-time

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