Data Science on the Google Cloud Platform Implementing End-to-end Real-time Data Pipelines : from Ingest to Machine Learning
Data Science on the Google Cloud Platform Implementing End-to-end Real-time Data Pipelines: from Ingest to Machine Learning by Valliappa Lakshmanan is published by O’Reilly Media in 2018 and spans 393 pages. This book serves as a practical guide for developers entering the data science field, illustrating how to leverage the Google Cloud Platform (GCP) to implement comprehensive data pipelines. It focuses on applying statistical and machine learning methods to real-world problems, providing readers with hands-on experience in building and managing data workflows.
Readers will find detailed instructions on automating data ingestion, creating dashboards, and conducting real-time analytics using GCP tools. The book covers various techniques, including building Bayesian models, logistic regression models with Spark, and high-performing prediction models with TensorFlow. By following the outlined processes, readers can enhance their understanding of data science while utilizing GCP’s collaborative features to tackle complex data challenges effectively.
Official synopsis Publisher
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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