ML Model Packaging & Deployment

The following repository is a small project undertaken to re-work and deploy a .ipynb machine learning notebook to production for real-time prediction.

To achieve this we:

• Transform our .ipynb into a Python package using Flit
• Work our cleaned package into an application using Flask
• Deploy our application to Heroku to be used as an API endpoint for ML predictions

Functionality

• All code is PEP8 compliant
• After training, model weights will be persisted
• If a model is not previously trained, any call of predict will train and save weights
• $ pytest can be used to run development tests on model.py during development and changes
• $ python app.py NNNN launches and runs our Flask application on port NNNN (host 0.0.0.0); alternatively, port can be specified on PORT environment variable (otherwise defaults to 5001)
• Error handling implemented to return custom status codes for 400, 404, 500 and incorrect model variables passed on URL parameters
• Heroku application is integrated with Github so that updates deploy to live endpoint: https://sample-ml-model-deploy.herokuapp.com/

.ipynb

The original .ipynb & dataset used to train this model can be found at /reference

Package Usage

To install from /library:

$ pip install .

Basic usage:

>>> from ie_bike_model.model import train_and_persist, predict
>>> train_and_persist()  # Trains the model and saves it to `model.joblib`
>>> predict(
...     dteday="2012-11-01",
...     hr=10,
...     weathersit="Clear, Few clouds, Partly cloudy, Partly cloudy"
...     temp=0.3,
...     atemp=0.31,
...     hum=0.8,
...     windspeed=0.0,
... )
105

Local Application

$ pip install -r requirements.txt
$ python app.py 5000

Training: Open http://0.0.0.0:5000/train in your browser.

Sample Prediction: http://0.0.0.0:5000/predict?date=2012-11-01&hour=10&weather_situation=clear&temperature=0.3&feeling_temperature=0.31&humidity=0.8&windspeed=0.0

Deployed Rest API

Training: https://sample-ml-model-deploy.herokuapp.com/train

Sample Prediction: https://sample-ml-model-deploy.herokuapp.com/predict?date=2012-11-01&hour=10&weather_situation=clear&temperature=0.3&feeling_temperature=0.31&humidity=0.8&windspeed=0.0