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Using Python Flask, I wanted to create a simple web API that allows users to upload a dataset, choose one or more models, store them server side, and then hit an endpoint to get a prediction.
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data_tools
datasets
ml_models
saved_formats
saved_models
.gitignore
app.py
mail_gun.py
readme.md
test.py

readme.md

Project Goal

I want to build a simple to use API that allows users to upload a dataset, generate multiple machine learning models and receive an endpoint at which to make predictions in the future

Project Structure

  • app.py - Flask API file
  • test.py - Used for testing ML model stuff in console application
  • data_tools/
    • csv_tools.py - tools for loading and parsing a CSV
    • loading.py - tools for saving and loading persistent ML models
  • datasets/
    • Keeps all datasets uploaded to API. Will be transitioning to some sort of blob storage
  • ml_models/
    • sklearn.py - Wrapper for any sklearn models we decide to support
    • my_models.py - Wrapper for any custom ML models we want to implement
  • saved_models/
    • Used as storage of persistent ML models
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