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End-to-End Machine Learning

Deploy a Predictive Model

This project is part of the liveProject series End-to-End Machine Learning for Rain Prediction.
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.

book and video resources

When you start your liveProject, you get full access to the following books and videos for 90 days.

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:

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

you will learn

In this liveProject, you’ll make a proven-and-tested machine learning model easily available in production to non-technical users.

  • Creation and maintenance of a virtual environment
  • GET and POST requests
  • Creating a web framework for your weather prediction application
  • Saving and loading of sklearn objects
  • Remote deployment on Heroku

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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