Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning
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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 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
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Publisher : O’Reilly Media; 2nd edition (May 3, 2022)
Language : English
Paperback : 459 pages
ISBN-10 : 1098118952
ISBN-13 : 978-1098118952
Item Weight : 2.31 pounds
Dimensions : 7 x 1 x 9.25 inches
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6 reviews for Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning
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Sachin Agarwal –
Truly excellent real world examples
The book does a wonderful job of starting with examples of jobs to be done, showing the right Google Cloud tools for the job, and then showing *why* they work well together. I found this book to be very clear about what it is and being smart to not try to shove in extraneous stuff – highly recommended.
Sagar –
Great book with a case study to follow along
This is a great book and hands on practice makes the learning real. Author also presents many of the choices considered and does a great jobof explaining why a choice was made . I would highly recommend it
Andrey Pomortsev –
Must read
Still in my list to read, but I believe in the author, so it’s five stars.
Kashaf Khan –
Having done the certifications , I can say this is the best book I have come across to explain end to end use of gcp as simple as is possible. The author heads covers the latest services in gcp inc vertex
pavanish kumar –
Book nicely takes you on a data science ride with thoughtfully organized chapters. These chapters are well connected and incremental in developing the knowledge of data science stack on GCP or on any cloud or even on-premise data stack. Dr. Valliappa Lakshmanan brings in his researcher/Scientist hat for writing the book. Each chapter nicely starts with a problem statement and approach to solving it. Most of the technical books are nice documentation and have no link between chapters. Each chapter uses different toy data sets to show different stacks. Data science on GCP uses the same data set to demonstrate how you think and solve the real problem. Dr.Lakshmanan uses a storytelling approach to tie various concepts and tools. However, it may not suit beginners in data science.
Matt Turner –
Love this book great content and structure with good worked examples. A must for all DS practitioners who want to get ahead on the Google AI/ML platform.