Overview

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 CloudAutomate and schedule data ingest using Cloud RunCreate and populate a dashboard in Data StudioBuild a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQueryConduct interactive data exploration with BigQueryCreate a Bayesian model with Spark on Cloud DataprocForecast time series and do anomaly detection with BigQuery MLAggregate within time windows with DataflowTrain explainable machine learning models with Vertex AIOperationalize ML with Vertex AI Pipelines

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9781098118952

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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.

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