Data Science on the Google Cloud Platform

"Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP.Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way."

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Intro -- Copyright -- Table of Contents -- Preface -- Who This Book Is For -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Making Better Decisions Based on Data -- Many Similar Decisions -- The Role of Data Scientists -- Scrappy Environment -- Full Stack Cloud Data Scientists -- Collaboration -- Best Practices -- Simple to Complex Solutions -- Cloud Computing -- Serverless -- A Probabilistic Decision -- Probabilistic Approach -- Probability Density Function -- Cumulative Distribution Function

Choices Made -- Choosing Cloud -- Not a Reference Book -- Getting Started with the Code -- Agile Architecture for Data Science on Google Cloud -- What Is Agile Architecture? -- No-Code, Low-Code -- Use Managed Services -- Summary -- Suggested Resources -- Chapter 2. Ingesting Data into the Cloud -- Airline On-Time Performance Data -- Knowability -- Causality -- Training-Serving Skew -- Downloading Data -- Hub-and-Spoke Architecture -- Dataset Fields -- Separation of Compute and Storage -- Scaling Up -- Scaling Out with Shared Data -- Scaling Out with Data-in-Place -- Ingesting Data

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