Data Science on the Google Cloud Platform

Preface

In my current role at Google, I get to work alongside data scientists and data engineers in a variety of industries as they move their data processing and analysis methods to the public cloud. Some try to do the same things they do on-premises, the same way they do them, just on rented computing resources. The visionary users, though, rethink their systems, transform how they work with data, and thereby are able to innovate faster.

As early as 2011, an article in Harvard Business Review recognized that some of cloud computing’s greatest successes come from allowing groups and communities to work together in ways that were not previously possible. This is now much more widely recognized—an MIT survey in 2017 found that more respondents (45%) cited increased agility rather than cost savings (34%) as the reason to move to the public cloud.

In this book, we walk through an example of this new transformative, more collaborative way of doing data science. You will learn how to implement an end-to-end data pipeline—we will begin with ingesting the data in a serverless way and work our way through data exploration, dashboards, relational databases, and streaming data all the way to training and making operational a machine learning model. I cover all these aspects of data-based services because data engineers will be involved in designing the services, developing the statistical and machine learning models and implementing them in large-scale production and in real time.

Who ...