Note:  This is the 1st edition. There is now a Second Edition of this book available. 

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:
[*]         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

https://rapidgator.net/file/070ecfd588dfcd5a3bca148a31c4b3e0/