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You're reading from  Mastering Predictive Analytics with Python

Product typeBook
Published inAug 2016
Reading LevelIntermediate
Publisher
ISBN-139781785882715
Edition1st Edition
Languages
Python
Concepts
Predictive Analytics
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Author (1)
Joseph Babcock
Joseph Babcock
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Joseph Babcock

Joseph Babcock has spent more than a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Through his career he has worked on recommender systems, petabyte scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to the field of drug discovery and genomics.
Read more about Joseph Babcock

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The architecture of a prediction service

Now with a clear goal in mind—to share and scale the results of our predictive modeling using a web application—what are the components required to accomplish this objective?

The first is the client: this could be either a web browser or simply a user entering a curl command in the terminal (see Aside). In either case, the client sends requests using hypertext transfer protocol (HTTP), a standard transport convention to retrieve or transmit information over a network (Berners-Lee, Tim, Roy Fielding, and Henrik Frystyk. Hypertext transfer protocol--HTTP/1.0. No. RFC 1945. 1996). An important feature of the HTTP standard is that the client and server do not have to 'know' anything about how the other is implemented (for example, which programming language is used to write these components) because the message will remain consistent between them regardless by virtue of following the HTTP standard.

The next component is the server, which receives HTTP...

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Mastering Predictive Analytics with Python
Published in: Aug 2016Publisher: ISBN-13: 9781785882715
© 2016 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Joseph Babcock

Joseph Babcock has spent more than a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Through his career he has worked on recommender systems, petabyte scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to the field of drug discovery and genomics.
Read more about Joseph Babcock

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