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Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning by Valliappa Lakshmanan
English | December 12th, 2017 (2018 Edition) | ASIN: B0787L7RK3, ISBN: 1491974567 | 410 Pages | EPUB | 13.09 MB
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
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