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Monetizing Machine Learning
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Table of Contents

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


About the Author

Manuel Amunategui has decades of professional experience in programming, data science, and creating end-to-end solutions for customers in various industries. He sees informational and educational gaps in the industry. He has been fortunate to work with software at Microsoft, in finance on Wall Street, in research at one of the largest health systems in the US, and now as VP of Data Science at SpringML, a Google Cloud and Salesforce preferred partner. He understands what it takes to start new careers and new businesses.Since 2013, he has been advocating for data science through blogs, vlogs, and educational material. He has grown and curated various highly focused and niche social media channels, including a YouTube channel with 60 videos and 350k views and a very popular applied data science blog. His teaching perspective is about welcoming any new comer with a desire to learn, creating material to quickly overcome learning curves, and demonstrating through clear narrative and practical examples that it is never as hard as most people think. Mehdi Roopaei, PhD, is a postdoctoral fellow at Open Cloud Institute of University of Texas at San Antonio, with a research focus on data-driven decision-making systems. He has 12 years of experience in teaching at the university level, more than 980 citations for peer-reviewed publications, and two published books. His focus is on cloud machine learning, data analytics, and the AI-Thinking platform (proposed at HICSS51).

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