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2018 | Buch

Monetizing Machine Learning

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

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Über dieses Buch

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 ideasCreate 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 contentCreate dashboards with paywalls to offer subscription-based accessAccess 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.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction to Serverless Technologies
Abstract
We’re going to create a very simple Flask web application (Figure 1-1) that we will reuse in the next four sections when we explore cloud-based services from Amazon AWS, Google Cloud, Microsoft Azure, and Python Anywhere.
Manuel Amunategui, Mehdi Roopaei
Chapter 2. Client-Side Intelligence Using Regression Coefficients on Azure
Abstract
Let's build an interactive web application to understand bike rental demand using regression coefficients on Microsoft Azure.
Manuel Amunategui, Mehdi Roopaei
Chapter 3. Real-Time Intelligence with Logistic Regression on GCP
Abstract
Let’s understand who survived the Titanic shipwreck by building an interactive passenger profile builder on Google Cloud.
Manuel Amunategui, Mehdi Roopaei
Chapter 4. Pretrained Intelligence with Gradient Boosting Machine on AWS
Abstract
What makes a top-rated wine? Find out with a hard-to-resist real-time web dashboard on Amazon Web Services.
Manuel Amunategui, Mehdi Roopaei
Chapter 5. Case Study Part 1: Supporting Both Web and Mobile Browsers
Abstract
Predicting the stock market with web and mobile platforms support on PythonAnywhere.com.
Manuel Amunategui, Mehdi Roopaei
Chapter 6. Displaying Predictions with Google Maps on Azure
Abstract
Where will crime happen next in San Francisco? Let’s build an interactive predictive mapping dashboard using Google Maps and Microsoft Azure.
Manuel Amunategui, Mehdi Roopaei
Chapter 7. Forecasting with Naive Bayes and OpenWeather on AWS
Abstract
Will I golf tomorrow? Find out using naive Bayes and real-time weather forecasts on Amazon Web Services.
Manuel Amunategui, Mehdi Roopaei
Chapter 8. Interactive Drawing Canvas and Digit Predictions Using TensorFlow on GCP
Abstract
Let’s build an interactive drawing canvas to enable visitors to draw and predict digits using TensorFlow image classification on Google Cloud.
Manuel Amunategui, Mehdi Roopaei
Chapter 9. Case Study Part 2: Displaying Dynamic Charts
Abstract
Displaying dynamic stock charts on PythonAnywhere.
Manuel Amunategui, Mehdi Roopaei
Chapter 10. Recommending with Singular Value Decomposition on GCP
Abstract
What to watch next? Let's recommend movie options using SVD and the Wikipedia API on Google Cloud.
Manuel Amunategui, Mehdi Roopaei
Chapter 11. Simplifying Complex Concepts with NLP and Visualization on Azure
Abstract
Let's build a simple interactive dashboard to understand the cost of eliminating spam messages using natural language processing on Microsoft Azure.
Manuel Amunategui, Mehdi Roopaei
Chapter 12. Case Study Part 3: Enriching Content with Fundamental Financial Information
Abstract
Predicting the stock market with fundamental financial data aggregation on PythonAnywhere.
Manuel Amunategui, Mehdi Roopaei
Chapter 13. Google Analytics
Abstract
Advanced intelligence for free.
Manuel Amunategui, Mehdi Roopaei
Chapter 14. A/B Testing on PythonAnywhere and MySQL
Abstract
This is an ambitious chapter, so we’ll limit the scope in order to distill the essence of this rich topic without going overboard. We’ll start by building a simple MySQL database and table to track whether a visitor liked or didn’t like the art work on the landing page. Because this is A/B Testing, we’re going to create two landing pages and switch them randomly when users visit the site (Figure 14-1).
Manuel Amunategui, Mehdi Roopaei
Chapter 15. From Visitor to Subscriber
Abstract
A look at some simple authentication schemes.
Manuel Amunategui, Mehdi Roopaei
Chapter 16. Case Study Part 4: Building a Subscription Paywall with Memberful
Abstract
Let's finalize our case study with a subscription-based paywall using Memberful and credit card payments on PythonAnywhere.
Manuel Amunategui, Mehdi Roopaei
Chapter 17. Conclusion
Abstract
The coverage in this book is ambitious and sacrifices had to be made, sections had to be omitted. What it may lack in technology introductions is hopefully made up for by quickly getting you up and running, and providing pointers on where to look for additional information. We only briefly covered databases and didn’t cover custom domain names; thankfully plenty of others have written about that already.
Manuel Amunategui, Mehdi Roopaei
Backmatter
Metadaten
Titel
Monetizing Machine Learning
verfasst von
Manuel Amunategui
Mehdi Roopaei
Copyright-Jahr
2018
Verlag
Apress
Electronic ISBN
978-1-4842-3873-8
Print ISBN
978-1-4842-3872-1
DOI
https://doi.org/10.1007/978-1-4842-3873-8