Data science on the Google cloud platform : implementing end-to-end real-time data pipelines : from ingest to machine learning / Valliappa Lakshmanan.
Material type: TextPublisher: Sebastopol, CA : O'Reilly Media, ©2018Edition: First editionDescription: xiv, 393 pages : illustrations ; 24 cmContent type:- text
- still image
- unmediated
- volume
- 1491974567
- 9781491974568
- 004.33 23
- QA76.54 LAK
Item type | Current library | Collection | Call number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|
Books | GSU Library Epoch General Stacks | Non-fiction | QA76.54LAK (Browse shelf(Opens below)) | Available | 50000005854 |
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Includes index.
Making better decisions based on data -- Ingesting data into the cloud -- Creating compelling dashboards -- Streaming data: publication and ingest -- Interactive data exploration -- Bayes classifier on cloud dataproc -- Machine learning: logistic regression on Spark -- Time-windowed aggregate features -- Machine learning classifier using TensorFlow -- Real-time machine learning.
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. Over 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.-- Source other than the Library of Congress.
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