ML Deployment Configuration

This repository explains the steps how a jupyter notebook is converted into a format that is used during machine learning deployment. The notebook is converted into 3 files. For this project, I have used TITANIC case study to convert the solution notebook into the below three files.

1. CONFIG FILE:

   It has information such as link to dataset, variable groups 
   to be used in the preprocessing and modeling step

2. PREPROCESSING FILE:

   It has information of all the definition required to preprocess the files. 
   It has a pipeline class that contains 3 important functions : FIT, TRANSFORM & PREDICT

3. PIPELINE FILE:

   In this file, we will call python utilities such as config.py and preocessing.py 
   to use pipeline class and methods insided it that is FIT, TRANSFORM & PREDICT