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Monetizing Machine Learning

Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud

  • Manuel Amunategui
  • Mehdi Roopaei

Table of contents

  1. Front Matter
    Pages i-xli
  2. Manuel Amunategui, Mehdi Roopaei
    Pages 1-37
  3. Manuel Amunategui, Mehdi Roopaei
    Pages 39-91
  4. Manuel Amunategui, Mehdi Roopaei
    Pages 93-127
  5. Manuel Amunategui, Mehdi Roopaei
    Pages 129-166
  6. Manuel Amunategui, Mehdi Roopaei
    Pages 167-193
  7. Manuel Amunategui, Mehdi Roopaei
    Pages 195-235
  8. Manuel Amunategui, Mehdi Roopaei
    Pages 237-261
  9. Manuel Amunategui, Mehdi Roopaei
    Pages 263-288
  10. Manuel Amunategui, Mehdi Roopaei
    Pages 289-303
  11. Manuel Amunategui, Mehdi Roopaei
    Pages 305-340
  12. Manuel Amunategui, Mehdi Roopaei
    Pages 341-374
  13. Manuel Amunategui, Mehdi Roopaei
    Pages 393-399
  14. Manuel Amunategui, Mehdi Roopaei
    Pages 401-424
  15. Manuel Amunategui, Mehdi Roopaei
    Pages 425-447
  16. Manuel Amunategui, Mehdi Roopaei
    Pages 449-469
  17. Manuel Amunategui, Mehdi Roopaei
    Pages 471-476
  18. Back Matter
    Pages 477-482

About this book

Introduction

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.

What You’ll Learn:

  • Extend your machine learning models using simple techniques to create compelling and interactive web dashboards
  • Leverage the Flask web framework for rapid prototyping of your Python models and ideas
  • Create dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more
  • Harness the power of TensorFlow by exporting saved models into web applications
  • Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored content
  • Create dashboards with paywalls to offer subscription-based access
  • Access API data such as Google Maps, OpenWeather, etc.
  • Apply different approaches to make sense of text data and return customized intelligence
  • Build an intuitive and useful recommendation site to add value to users and entice them to keep coming back
  • Utilize the freemium offerings of Google Analytics and analyze the results
  • Take your ideas all the way to your customer's plate using the top serverless cloud providers

Keywords

Machine learning Machine Intelligence TensorFlow Deep learning Google Cloud Platform Cloud computing Web Application Python Cloud Hosting Serverless Flask Modeling Small Business Natural Language Processing NLP Subscription Web Site

Authors and affiliations

  • Manuel Amunategui
    • 1
  • Mehdi Roopaei
    • 2
  1. 1.PortlandUSA
  2. 2.PlattevilleUSA

Bibliographic information