Absolute Beginner's Guide to Deploy ML model with Flask (Part-1)

This page summarizes the projects mentioned and recommended in the original post on dev.to

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  • pyenv

    Simple Python version management

  • pyenv-virtualenv

    a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv)

  • https://github.com/pyenv/pyenv-virtualenv - This plugin makes managing multiple Python virtual environments on Unix like systems so much easier. You can use Conda as well for setting up your virtual environment.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • The entire project is uploaded in my GitHub repository on the branch Project_HeartDiseasPrediction, where you can see the entire html code as I didn’t write the incorporated version in the end - https://github.com/Afroza2/Production-Based-ML-portfolio/tree/Project_HeartDiseasePrediction. The dataframe that you passed as the argument of the prediction function can now be created from user input and the output will be shown in the modal. For Part-2, I am planning to write on how to host this entire project as a Heroku application so that you can access the project online. Till then, bonk!

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