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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 Pyth...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[New York] :
Apress,
[2018]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Amunategui, Manuel, |e author. | |
| 245 | 1 | 0 | |a Monetizing machine learning : |b quickly turn Python ML ideas into web applications on the serverless cloud / |c Manuel Amunategui, Mehdi Roopaei. |
| 264 | 1 | |a [New York] : |b Apress, |c [2018] | |
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| 520 | |a 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. | ||
| 588 | 0 | |a Online resource; title from digital title page (viewed on October 08, 2018). | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Intro; Table of Contents; About the Authors; About the Technical Reviewers; Acknowledgments; Introduction; Chapter 1: Introduction to Serverless Technologies; A Simple Local Flask Application; Step 1: Basic "Hello World!" Example; Step 2: Start a Virtual Environment; Step 3: Install Flask; Step 4: Run Web Application; Step 5: View in Browser; Step 6: A Slightly Faster Way; Step 7: Closing It All Down; Introducing Serverless Hosting on Microsoft Azure; Step 1: Get an Account on Microsoft Azure; Step 2: Download Source Files; Supporting Files; Step 3: Install Git; Step 4: Open Azure Cloud Shell. | |
| 505 | 8 | |a Step 5: Create a Deployment UserStep 6: Create a Resource Group; Step 7: Create an Azure Service Plan; Step 8: Create a Web App; Check Your Website Placeholder; Step 9: Pushing Out the Web Application; Step 10: View in Browser; Step 11: Don't Forget to Delete Your Web Application!; Conclusion and Additional Information; Introducing Serverless Hosting on Google Cloud; Step 1: Get an Account on Google Cloud; Step 2: Download Source Files; Step 3: Open Google Cloud Shell; Step 4: Upload Flask Files to Google Cloud; Step 5: Deploy Your Web Application on Google Cloud. | |
| 505 | 8 | |a Step 6: Don't Forget to Delete Your Web Application!Conclusion and Additional Information; Introducing Serverless Hosting on Amazon AWS; Step 1: Get an Account on Amazon AWS; Step 2: Download Source Files; Step 3: Create an Access Account for Elastic Beanstalk; Step 4: Install Elastic Beanstalk (EB); Step 5: EB Command Line Interface; Step 6: Take if for a Spin; Step 7: Don't Forget to Turn It Off!; Conclusion and Additional Information; Introducing Hosting on PythonAnywhere; Step 1: Get an Account on PythonAnywhere; Step 2: Set Up Flask Web Framework; Conclusion and Additional Information. | |
| 505 | 8 | |a Creating Dummy Features from Categorical DataTrying a Nonlinear Model; Even More Complex Feature Engineering-Leveraging Time-Series; A Parsimonious Model; Extracting Regression Coefficients from a Simple Model-an Easy Way to Predict Demand without Server-Side Computing; R-Squared; Predicting on New Data Using Extracted Coefficients; Designing a Fun and Interactive Web Application to Illustrate Bike Rental Demand; Abstracting Code for Readability and Extendibility; Building a Local Flask Application; Downloading and Running the Bike Sharing GitHub Code Locally; Debugging Tips. | |
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