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

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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 a...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Lakshmanan, Valliappa (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, 2018.
Edición:First edition.
Temas:
Google (Firm)
Real-time data processing.
Cloud computing.
Computing platforms.
Temps réel (Informatique)
Infonuagique.
Plateformes (Informatique)
COMPUTERS > Computer Literacy.
COMPUTERS > Computer Science.
COMPUTERS > Data Processing.
COMPUTERS > Hardware > General.
COMPUTERS > Information Technology.
COMPUTERS > Machine Theory.
COMPUTERS > Reference.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Descripción
Sumario: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.
Notas:Includes index.
Descripción Física:1 online resource (xiv, 393 pages) : illustrations
ISBN:9781491974537
1491974532
9781491974513
1491974516

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