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210123 ||| eng |
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|a 9781491974513
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|a 1491974532
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|a 9781491974537
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|a QA76.54
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| 100 |
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|a Lakshmanan, Valliappa
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| 245 |
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|a Data science on the Google cloud platform
|b implementing end-to-end real-time data pipelines: from ingest to machine learning
|c Valliappa Lakshmanan
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| 250 |
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|a First edition
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| 260 |
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|a Sebastopol, CA
|b O'Reilly Media
|c 2018
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| 300 |
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|a xiv, 393 pages
|b illustrations
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| 505 |
0 |
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|a 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
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| 653 |
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|a COMPUTERS / Computer Science / bisacsh
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|a Cloud computing / fast
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|a Google (Firm) / fast
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|a Infonuagique
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| 653 |
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|a COMPUTERS / Hardware / General / bisacsh
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| 653 |
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|a Computing platforms / fast
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| 653 |
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|a COMPUTERS / Data Processing / bisacsh
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|a COMPUTERS / Reference / bisacsh
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|a Real-time data processing / http://id.loc.gov/authorities/subjects/sh85111765
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|a Temps réel (Informatique)
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|a Computing platforms / http://id.loc.gov/authorities/subjects/sh2011003111
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| 653 |
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|a Cloud computing / http://id.loc.gov/authorities/subjects/sh2008004883
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|a COMPUTERS / Computer Literacy / bisacsh
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|a Real-time data processing / fast
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| 653 |
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|a Plateformes (Informatique)
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| 653 |
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|a Google (Firm) / http://id.loc.gov/authorities/names/no00095539
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|a COMPUTERS / Machine Theory / bisacsh
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|a COMPUTERS / Information Technology / bisacsh
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Includes index
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| 015 |
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|a GBB7B3690
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| 776 |
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|z 9781491974537
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|z 9781491974513
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|u https://learning.oreilly.com/library/view/~/9781491974551/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 004.33
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|a 500
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| 520 |
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|a 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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