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.

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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