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Mastering Predictive Analytics with Python

You're reading from  Mastering Predictive Analytics with Python

Product type Book
Published in Aug 2016
Publisher
ISBN-13 9781785882715
Pages 334 pages
Edition 1st Edition
Languages
Author (1):
Joseph Babcock Joseph Babcock
Profile icon Joseph Babcock
Toc

Table of Contents (16) Chapters close

Mastering Predictive Analytics with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. From Data to Decisions – Getting Started with Analytic Applications 2. Exploratory Data Analysis and Visualization in Python 3. Finding Patterns in the Noise – Clustering and Unsupervised Learning 4. Connecting the Dots with Models – Regression Methods 5. Putting Data in its Place – Classification Methods and Analysis 6. Words and Pixels – Working with Unstructured Data 7. Learning from the Bottom Up – Deep Networks and Unsupervised Features 8. Sharing Models with Prediction Services 9. Reporting and Testing – Iterating on Analytic Systems Index

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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