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Data Science on the Google Cloud Platform : Implementing End-To-End Real-Time Data Pipelines: from Ingest to Machine Learning by Valliappa Lakshmanan (2018, Trade Paperback)

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Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1491974567
ISBN-13
9781491974568
eBay Product ID (ePID)
235117281

Product Key Features

Number of Pages
402 Pages
Publication Name
Data Science on the Google Cloud Platform : Implementing End-To-End Real-Time Data Pipelines: from Ingest to Machine Learning
Language
English
Publication Year
2018
Subject
Systems Architecture / Distributed Systems & Computing, Data Modeling & Design, Data Processing, Databases / General
Type
Textbook
Author
Valliappa Lakshmanan
Subject Area
Computers
Format
Trade Paperback

Dimensions

Item Height
0.8 in
Item Weight
24.1 Oz
Item Length
9.2 in
Item Width
7 in

Additional Product Features

Intended Audience
Trade
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
004.6782
Synopsis
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, 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
LC Classification Number
QA76.585

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Data Science on the Google Cloud Platform : Implementing End-To-End Real-Time Data Pipelines: from Ingest to Machine Learning by Valliappa Lakshmanan (2018, Trade Paperback)

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artículo 1 Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time...

artículo 2 Data Science on the Google Cloud Platform : Implementing End-To-End Real-Time...

artículo 3 Data Science on the Google Cloud Platform : Implementing End-To-E

artículo 4 Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Dat

artículo 5 Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Dat

artículo 6 Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time

artículo 7 Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Da,

artículo 8 Data Science on the Google Cloud Platform by Valliappa Lakshmanan

artículo 9 Data Science on the Google Cloud Platform : Implementing Ent-to-End Real-Time...

artículo 10 Data Science on the Google Cloud Platform : Implementing Ent-to-End Real-Time...

artículo 11 DATA SCIENCE ON THE GOOGLE CLOUD PLATFORM: IMPLEMENTING By Valliappa Lakshmanan

artículo 12 Data Science on the Google Cloud Platform: Implementing End-to-E

artículo 13 Data Science on the Google Cloud Platform: Implementing End-To-End Real-Time

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