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

by Valliappa Lakshmanan

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

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

Genres: Technology

464 pages, Kindle Edition

Published March 29, 2022

Reviews

[Chaouki · Rating 5 out of 5]

In a time where every ML book has the fashion mnist and god awful repetitive themes, this book stands head and shoulders above any book i have read in it's style and concept presentation. Like an expensive perfume that when you smell you say it is worth every penny. This book is a breeze. If you've had enough of toy datasets and want to get to know, really get to know Google cloud , this book is a great pic. Buy and thank me later.

[Josua Naiborhu · Rating 5 out of 5]

I am amazed by how the author explained the concept in a concise way with the hands-on use case. really loved this book

[Ntombizakhona Mabaso · Rating 5 out of 5]

This book dives deep into tools like BigQuery and TensorFlow, making it an excellent resource for data science enthusiasts.

While I found it quite lengthy (perhaps because data science isn’t my primary interest), I appreciated the hands-on code examples and the thoughtful suggestions, resources, and articles provided throughout.

The appendix on 'Considerations for Sensitive Data within Machine Learning Datasets' is particularly noteworthy—worth revisiting multiple times for its invaluable insights. A comprehensive guide for those looking to master data science on Google Cloud.