Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning 2nd

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Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning 2nd

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內容描述

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
Operationalize ML with Vertex AI Pipelines

作者介紹

Valliappa (Lak) Lakshmanan is the director of analytics and AI solutions at Google Cloud, where he leads a team building cross-industry solutions to business problems. His mission is to democratize machine learning so that it can be done by anyone anywhere. Lak is the author or coauthor of Practical Machine Learning for Computer Vision, Machine Learning Design Patterns, Data Governance The Definitive Guide, Google BigQuery The Definitive Guide, and Data Science on the Google Cloud Platform.