Data Science on the Google Cloud Platform (O'Reilly)
Data Science on the Google Cloud Platform (O'Reilly)
Monday, 19 February 2018

This book shows how to apply sophisticated statistical and machine learning methods to real-world problems when building on top of the Google Cloud Platform. Author Valliappa Lakshmanan 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 the Google Cloud Platform. Throughout the book you’ll work through a sample business decision by employing a variety of data science approaches.

<ASIN:1491974567>

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.

Author: Valliappa Lakshmanan
Publisher: O'Reilly
Date: Jan 2018
Pages: 410
ISBN: 978-1491974568
Print: 1491974567
Kindle: B0787L7RK3
Audience: data scientists
Level: intermediate
Category: Data Science

 

  • Automate and schedule data ingest, using an App Engine application
  • Create and populate a dashboard in Google Data Studio
  • Build a real-time analysis pipeline to carry out streaming analytics
  • Conduct interactive data exploration with Google BigQuery
  • Create a Bayesian model on a Cloud Dataproc cluster
  • Build a logistic regression machine-learning model with Spark
  • Compute time-aggregate features with a Cloud Dataflow pipeline
  • Create a high-performing prediction model with TensorFlow
  • Use your deployed model as a microservice you can access from both batch and real-time pipelines

For recommendations of Big Data books see Reading Your Way Into Big Data in our Programmer's Bookshelf section.

Book Watch is I Programmer's listing of new books and is compiled using publishers' publicity material. It is not to be read as a review where we provide an independent assessment. Some, but by no means all, of the books in Book Watch are eventually reviewed.

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ISBN: 978-1484211403
Print: 1484211405
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