Monetizing machine learning : quickly turn Python ML ideas into web applications on the serverless cloud /: quickly turn Python ML ideas into web applications on the serverless cloud. (2018)

• Record Type: Book
• Further Information: Note: Manuel Amunategui, Mehdi Roopaei.
• Authors: Amunategui, Manuel; Roopaei, Mehdi
• Contents: 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 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 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 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
• Publisher Details: Place of publication not identified : Apress
• Publication Date: 2018
• Extent: 1 online resource
• Subjects: 006.3/12; Computer science; Machine learning; Computer algorithms; Python (Computer program language); COMPUTERS / General; Computers -- Hardware -- Network Hardware; Computers -- Database Management -- General; Network hardware; Databases; Electronic data processing; Computer Communication Networks; Big data; Computers -- Computer Science; Program concepts / learning to program; Electronic books
• Languages: English
• ISBNs: 9781484238738; 1484238737
• Related ISBNs: 9781484238721
• Notes: Note: Online resource; title from PDF file page (EBSCO, viewed September 19, 2018).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.