Data Science on the Google Cloud Platform: Implementing end-to-end real-time data pipelines: from ingest to machine learning
Language: English
Published by O'Reilly Media, United States, Sebastopol, 2018
- Softcover
- Used
Seller: WorldofBooks, Goring-By-Sea, WS, United KingdomWorldofBooks
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Add to basketItem description from seller
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, 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: Automate and schedule data ingest, using an App Engine application Create and populate a dashboard in Google Data Studio Build a real-time analysis pipeline to carry out streaming analytics Conduct interactive data exploration with Google BigQuery Create a Bayesian model on a Cloud Dataproc cluster Build a logistic regression machine-learning model with Spark Compute time-aggregate features with a Cloud Dataflow pipeline Create a high-performing prediction model with TensorFlow Use your deployed model as a microservice you can access from both batch and real-time pipelines. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.…
Seller Inventory # GOR009966304
- Title
- Data Science on the Google Cloud Platform: Implementing end-to-end real-time data pipelines: from ingest to machine learning
- Author
- Valliappa Lakshmanan
- Publisher
- O'Reilly Media, United States, Sebastopol
- Publication year
- 2018
- Condition
- Very Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1491974567
- ISBN 13
- 9781491974568
- Item weight
- 703 grams
- Dimensions
- 181.0 x 234.0
Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud Platform (GCP). This hands-on guide shows developers entering the data science field how to implement an end-to-end data pipeline, using statistical and machine learning methods and tools on GCP. Through the course of the book, 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:
- Automate and schedule data ingest, using an App Engine application
- Create and populate a dashboard in Google Data Studio
- Build a real-time analysis pipeline to carry out streaming analytics
- Conduct interactive data exploration with Google BigQuery
- Create a Bayesian model on a Cloud Dataproc cluster
- Build a logistic regression machine-learning model with Spark
- Compute time-aggregate features with a Cloud Dataflow pipeline
- Create a high-performing prediction model with TensorFlow
- Use your deployed model as a microservice you can access from both batch and real-time pipelines
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About the Author
"About the title" may belong to another edition of this title.
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