ML-Ops Application

Group 11

Members:

1. Pavan Krishna (iampavankrishna@gmail.com)

2. Sanjog Patil (sanjogpatil@hotmail.com)

This repository contains code which demonstrates ML-Ops using a FastAPI application which predicts the defects class using the bug_pred.csv

Running Instructions

• Install dependencies using pip3 install -r requirements.txt
• Run application using python3 main.py
• Run tests using pytest

CI/CD

• build (test) for all the pull requests
• build (test) and upload_zip for all pushes

Group_11_Bug_Prediction_Application_Visualizatio_Explainability.ipynb

• Contains the working ML Model with Visualization and Explainability
• Visualization is done using Seaborn library/package
• Explainability is done using LIME and h2o AutoML

main.py

• Implements the FastAPI

ml_utils.py

• Implements the ML model

test_app.py

• Implements usage of pytest
• It helps us in checking the CI/CD process( whether it passes or fails)