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

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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.

Author(s): Manuel Amunategui, Mehdi Roopaei
Edition: 1
Publisher: Apress
Year: 2018

Language: English
Commentary: True PDF
Pages: 482
Tags: Google Cloud Platform; Amazon Web Services; Microsoft Azure; Cloud Computing; Artificial Intelligence; Machine Learning; Natural Language Processing; Regression; Python; Recommender Systems; Business; Web Applications; Data Visualization; MySQL; Finance; Logistic Regression; PythonAnywhere; Flask; Google Maps; OpenWeather; Google Analytics; Monetization;Языки программирования;Программирование;Python

Front Matter ....Pages i-xli
Introduction to Serverless Technologies (Manuel Amunategui, Mehdi Roopaei)....Pages 1-37
Client-Side Intelligence Using Regression Coefficients on Azure (Manuel Amunategui, Mehdi Roopaei)....Pages 39-91
Real-Time Intelligence with Logistic Regression on GCP (Manuel Amunategui, Mehdi Roopaei)....Pages 93-127
Pretrained Intelligence with Gradient Boosting Machine on AWS (Manuel Amunategui, Mehdi Roopaei)....Pages 129-166
Case Study Part 1: Supporting Both Web and Mobile Browsers (Manuel Amunategui, Mehdi Roopaei)....Pages 167-193
Displaying Predictions with Google Maps on Azure (Manuel Amunategui, Mehdi Roopaei)....Pages 195-235
Forecasting with Naive Bayes and OpenWeather on AWS (Manuel Amunategui, Mehdi Roopaei)....Pages 237-261
Interactive Drawing Canvas and Digit Predictions Using TensorFlow on GCP (Manuel Amunategui, Mehdi Roopaei)....Pages 263-288
Case Study Part 2: Displaying Dynamic Charts (Manuel Amunategui, Mehdi Roopaei)....Pages 289-303
Recommending with Singular Value Decomposition on GCP (Manuel Amunategui, Mehdi Roopaei)....Pages 305-340
Simplifying Complex Concepts with NLP and Visualization on Azure (Manuel Amunategui, Mehdi Roopaei)....Pages 341-374
Case Study Part 3: Enriching Content with Fundamental Financial Information (Manuel Amunategui, Mehdi Roopaei)....Pages 375-391
Google Analytics (Manuel Amunategui, Mehdi Roopaei)....Pages 393-399
A/B Testing on PythonAnywhere and MySQL (Manuel Amunategui, Mehdi Roopaei)....Pages 401-424
From Visitor to Subscriber (Manuel Amunategui, Mehdi Roopaei)....Pages 425-447
Case Study Part 4: Building a Subscription Paywall with Memberful (Manuel Amunategui, Mehdi Roopaei)....Pages 449-469
Conclusion (Manuel Amunategui, Mehdi Roopaei)....Pages 471-476
Back Matter ....Pages 477-482