大葉大學圖書館 |

登入

回首頁

Data science on the Google Cloud Pla...

Google (Firm)

Data science on the Google Cloud Platform :implementing end-to-end real-time data pipelines : from ingest to machine learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	004.33
書名/作者:	Data science on the Google Cloud Platform : : implementing end-to-end real-time data pipelines : from ingest to machine learning // Valliappa Lakshmanan.
作者:	Lakshmanan, Valliappa.
出版者:	Sebastopol, CA : : O'Reilly Media,, 2018.
面頁冊數:	xiv, 393 p. : : ill. ;; 24 cm.
附註:	Includes index.
標題:	Real-time data processing.
標題:	Cloud computing.
標題:	Computing platforms.
ISBN:	9781491974568 (pbk.) :
摘要、提要註:	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.

Data science on the Google Cloud Platform :implementing end-to-end real-time data pipelines : from ingest to machine learning /
Lakshmanan, Valliappa.

Data science on the Google Cloud Platform :implementing end-to-end real-time data pipelines : from ingest to machine learning /Valliappa Lakshmanan. - 1st ed. - Sebastopol, CA :O'Reilly Media,2018. - xiv, 393 p. :ill. ;24 cm.

Includes index.

Making better decisions based on data --

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.

ISBN: 9781491974568 (pbk.) :NTD 2,158Subjects--Corporate Names:

350056
Google (Firm)
Subjects--Uniform Titles:

Google Apps.
Subjects--Topical Terms:

403442
Real-time data processing.

Dewey Class. No.: 004.33

Data science on the Google Cloud Platform :implementing end-to-end real-time data pipelines : from ingest to machine learning /
LDR:02287cam a2200181 a 4500 001 473742
005 20180123012043.0
008 180523s2018 caua 001 0 eng d
020 $a 9781491974568 (pbk.) : $c NTD 2,158
040 $a YDX $b eng $c YDX $d BDX $d BTCTA $d GK8 $d IVY $d DYU
041 0 $a eng
082 0 4 $a 004.33 $2 23
100 1 $a Lakshmanan, Valliappa. $3 627151
245 1 0 $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.
250 $a 1st ed.
260 $a Sebastopol, CA : $b O'Reilly Media, $c 2018.
300 $a xiv, 393 p. : $b ill. ; $c 24 cm.
500 $a Includes index.
505 0 0 $t Making better decisions based on data -- $t Ingesting data into the Cloud -- $t Creating compelling dashboards -- $t Streaming data: publication and ingest -- $t Interactive data exploration -- $t Bayes classifier on Cloud Dataproc -- $t Machine learning: logistic regression on Spark -- $t Time-windowed aggregate features -- $t Machine learning classifier using TensorFlow -- $t Real-time machine learning.
520 $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. 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.
610 2 0 $a Google (Firm) $3 350056
630 0 0 $a Google Apps. $3 452751
650 0 $a Real-time data processing. $3 403442
650 0 $a Cloud computing. $3 367267
650 0 $a Computing platforms. $3 683082