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.

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Publisher: Addison-Wesley
Pages: 400
ISBN: 978-0137889655
Print: 0137889658
Kindle: B0CLL1TGVT
Audience: Programmers wanting to learn Rust
Rating: 3
Reviewer: Mike James
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Author: Murat Erder, Pierre Pureur and Eoin Woods
Publisher: Addison-Wesley
Pages: 352
ISBN: 978-0136523567
Print: 0136523560
Kindle: ‎B08ZRTQGLJ
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Rating: 3
Reviewer: Kay Ewbank

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