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Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time Data Pipelines: From Ingest to Machine Learning
by Lakshmanan, Valliappa
Availability:
$74.68
Used
-
Trade Paperback
-
9781098118952
Overview
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, 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
- Format: TradePaperback
- Author: Lakshmanan, Valliappa
- ISBN: 9781098118952
- Condition: Used
- Dimensions: 9.19 x 0.93
- Number Of Pages: 459
- Publication Year: 2022
Language:
English