Monetizing machine learning : quickly turn Python ML ideas into web applications on the serverless cloud /

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Bibliographic Details
Author / Creator:Amunategui, Manuel, author.
Imprint:[New York] : Apress, [2018]
Description:1 online resource
Language:English
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11706051
Hidden Bibliographic Details
Other authors / contributors:Roopaei, Mehdi, author.
ISBN:9781484238738
1484238737
9781484238745
1484238745
9781484245576
1484245571
9781484238721
1484238729
Digital file characteristics:text file
PDF
Notes:Includes bibliographical references.
Online resource; title from digital title page (viewed on October 08, 2018).
Summary:Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book - Amazon, Microsoft, Google, and PythonAnywhere. You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time. Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book.
Other form:Print version: Amunategui, Manuel. Monetizing machine learning. [New York] : Apress, [2018] 1484238729 9781484238721
Standard no.:10.1007/978-1-4842-3873-8
10.1007/978-1-4842-3