Deploy a Predictive Model

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prerequisites
basics of pandas • basics of NumPy • basics of Joblib/Pickle • basics of Flask, Jupyter Notebook, Heroku, and pipenv/virtualenv
skills learned
create a virtual environment • GET and POST requests • create a web framework for a prediction app • remotely deploy an app on Heroku
Harshit Tyagi
1 week · 3-5 hours per week · INTERMEDIATE
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In this liveProject, you’ll deploy a machine learning model to production so it can be easily used by colleagues. You’ll create a virtual environment for deploying your application, use Flask to build a local web service that returns predictions, and Heroku for remote deployment.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Harshit Tyagi
Harshit Tyagi has helped over a thousand students master the fundamentals of programming and data science. In his roles at OpenClassrooms and Coding Ninjas, he leverages his technical expertise to conduct workshops and help students bring their course projects to the finish line. He also has a YouTube channel, where he covers fundamental concepts in data science and Python, interview tips, and more. In addition to focusing on data science education, Harshit has developed data processing algorithms with research scientists at Yale, MIT and UCLA.

prerequisites

This liveProject is for intermediate Python data scientists who want to expand their capabilities in effective production deployment. To begin this liveProject, you will need to be familiar with the following:

TOOLS
  • Basics of Pandas
  • Basics of NumPy
  • Basics of Joblib/Pickle
  • Basics of Flask
  • Jupyter Notebooks
  • Heroku
  • pipenv/virtualenv

Note: The final milestone of this project uses Heroku to the application. Heroko incurs a cost. There is intermittent use, and the Eco option ($5) will be sufficient to get the app working as Eco covers 1000 hours, and we will be using far less than that for this project.

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