Data Science on the Google Cloud Platform, 2nd Edition
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 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.
You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud; Automate and schedule data ingest using Cloud Run; Create and populate a dashboard in Data Studio; Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery; Conduct interactive data exploration with BigQuery; Create a Bayesian model with Spark on Cloud Dataproc; Forecast time series and do anomaly detection with BigQuery ML; Aggregate within time windows with Dataflow; Train explainable machine learning models with Vertex AI; Operationalize ML with Vertex AI Pipelines.
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