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- 393 Seiten
- 14 Lesestunden
Mehr zum Buch
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
Buchkauf
Data Science on the Google Cloud Platform, Valliappa Lakshmanan
- Sprache
- Erscheinungsdatum
- 2019
- Einband
- (Paperback),
- Buchzustand
- Gebraucht - Gut
- Preis
- 9,39 €inkl. MwSt.
Keiner hat bisher bewertet.
- Titel
- Data Science on the Google Cloud Platform
- Untertitel
- Implementing End-to-End Real-Time Data Pipelines - Updated for TensorFlow 2.0
- Sprache
- Englisch
- Autor*innen
- Valliappa Lakshmanan
- Verlag
- O'Reilly Media
- Erscheinungsdatum
- 2019
- Einband
- Paperback
- Seitenzahl
- 393
- ISBN10
- 1491974567
- ISBN13
- 9781491974568
- Reihe
- Schlagwörter
- Technologie, Fachliteratur, Künstliche Intelligenz, Datenbanken, Datenanalyse, Maschinelles Lernen, Google
- Beschreibung
- 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