Data Science on the Google Cloud Platform

• Verfasst von: Lakshmanan, Valliappa [VerfasserIn]
• Titel: Data Science on the Google Cloud Platform
• Verf.angabe: Lakshmanan, Valliappa
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: O'Reilly Media, Inc.
• Jahr: 2017
• Umfang: 1 online resource (408 pages)
• ISBN: 978-1-4919-7455-1
• ISBN: 1-4919-7455-9
• ISBN: 978-1-4919-7453-7
• ISBN: 1-4919-7453-2
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: Google Apps
• K10plus-PPN: 1907898573
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4611090086
• Lokale URL Inst.: Zum Volltext

Abstract

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