Data science on the Google cloud platform : implementing end-to-end real-time data pipelines : from ingest to machine learning / Valliappa Lakshmanan.
- 1 of 1 copy available at Evergreen Indiana.
0 current holds with 1 total copy.
|Location||Call Number / Copy Notes||Barcode||Shelving Location||Status||Due Date|
|Eckhart PL - Main||006.7 LAK (Text)||840191002697860||Adult Nonfiction - Upper Level||Available||-|
- ISBN: 1491974567
- ISBN: 9781491974568
- Physical Description: xiv, 393 pages : illustrations ; 24 cm
- Edition: First edition.
- Publisher: Sebastopol, CA : O'Reilly Media, 2018.
|Formatted Contents Note:||
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.
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Real-time data processing.