Monetizing Machine Learning

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Monetizing Machine Learning Front Cover

Monetizing Machine Learning

by Manuel Amunategui, Mehdi Roopaei
  • Length: 482 pages
  • Edition: 1st ed.
  • Publisher: Apress
  • Publication Date: 2018-12-09
  • ISBN-10: 1484238729
  • ISBN-13: 9781484238721
  • Sales Rank: #882065 (See Top 100 Books)
Description

Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud

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

Who This Book Is For

Those with some programming experience with Python, code editing, and access to an interpreter in working order. The book is geared toward entrepreneurs who want to get their ideas onto the web without breaking the bank, small companies without an IT staff, students wanting exposure and training, and for all data science professionals ready to take things to the next level.

Table of Contents

Chapter 1: Introduction to Serverless Technologies
Chapter 2: Client-Side Intelligence Using Regression Coefficients on Azure
Chapter 3: Real-Time Intelligence with Logistic Regression on GCP
Chapter 4: Pretrained Intelligence with Gradient Boosting Machine on AWS
Chapter 5: Case Study Part 1: Supporting Both Web and Mobile Browsers
Chapter 6: Displaying Predictions with Google Maps on Azure
Chapter 7: Forecasting with Naive Bayes and OpenWeather on AWS
Chapter 8: Interactive Drawing Canvas and Digit Predictions Using TensorFlow on GCP
Chapter 9: Case Study Part 2: Displaying Dynamic Charts
Chapter 10: Recommending with Singular Value Decomposition on GCP
Chapter 11: Simplifying Complex Concepts with NLP and Visualization on Azure
Chapter 12: Case Study Part 3: Enriching Content with Fundamental Financial Information
Chapter 13: Google Analytics
Chapter 14: A/B Testing on  PythonAnywhere and MySQL
Chapter 15: From Visitor to Subscriber
Chapter 16: Case Study Part 4: Building a Subscription Paywall with Memberful
Chapter 17: Conclusion

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Monetizing Machine Learning

 
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