FastAPI ML Deployment

This project trains a simple unsupervised ML model and uses it to make predictions via API. It uses Sklearn and FastAPI.

Requirements:

• Python
• Pipenv

Prepare

• Just run pipenv install and you're good to go

How to Run

• pipenv run python app.py Note: any file change in this folder will trigger a live reload of the server.
• To test your GET/POST methods, go to http://0.0.0.0:8000/docs; use the sample data in 'data'. The names should suggest the use case.

The data

The dataset is in the file movies.csv. It is a subset of 5k movies taken from kaggle.

The ML task

The task consists in creating a clustering of those movies based on their categories.

The API functionality

I built two post methods.

• The '/cluster' endpoint is for clustering a dataset end to end.
• The '/predict' endpoint is for using the '7-means-clustering-pkl' model to predict the cluster of a new data point.

Project Files

• The 'clustering.ipynb' notebook contains EDA and KMeans clustering implementation.
• The model is trained, saved and ready to use via 'kmeans-model.pkl'. In 'app.py',

Developer Notes

• I couldn't make the API accept null values in the input (yet) so I just saved non-null dataframe samples for testing the different post methods.
• You can test the functionality using the JSONs in the 'data' folder I created.
• In the 'data' folder, we have different JSON samples to test the API. E.g: 'movies-records.json' is simply a JSON of the initial movies data frame.