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English | 2021 | ISBN: 9781098118945 | 213 pages | PDF,EPUB | 9.38 MB
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, using statistical and machine learning methods and tools on GCP.
Through the course of this updated second edition, 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.
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
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