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

by Manuel Amunategui

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

Genres: Programming

526 pages, Paperback

Published September 15, 2018

Reviews

[Islomjon · Rating 5 out of 5]

If you finished learning basics of data science and machine learning, then start reading this book!

As a machine learning engineer, I was always interested how to integrate, deploy and present your results to public with responsive web page. In Monetizing Machine Learning, I found a solid introduction how to do this.

Throughout chapters, Manuel Amunategui, looks and analyzes different popular datasets, and more importantly deploys results in simple web applications with several cloud platforms such as Azure, AWS, GCP and PythonAnywhere. The book also contains exciting case study about trading and its development into serious project.

Also it is important to note that reader should not pay attention to the book itself only. There are provided additional materials such as source codes and web-page architecture in the github repository which is crucial to look through to clearly understand some hidden workflows.

[Loc Nguyen · Rating 5 out of 5]

Really great intro to data models as web applications - but this vision of covering web app framework, machine learning, AND cloud deployment all within one book leads to a slight lack of depth and growth in all three areas. One may be better served by using this book to survey their options for deploying ML models, then use other books to build depth.

[Alan Couzens · Rating 4 out of 5]

A really great, practical introduction into both building basic "real world" machine learning models and deploying them on the various cloud services. Very low on theory and high on practice. While some of the cloud services have changed since the book was published it's still a really helpful little guide to get started on the right track.