Data Science on the Google Cloud Platform
Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning
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.
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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