Data Science on the Google Cloud Platform, 2nd Edition

Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

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    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
    Valliappa Lakshmanan
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    Table of Contents (9789355422095_toc.pdf, 186 Kb) [Download]

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