End to End MLOPs pipeline using MLflow, DVC, Python , Flask- Dataset Used - Wine Quality Prediction Dataset (https://www.kaggle.com/datasets/rajyellow46/wine-quality) Reference - https://www.youtube.com/playlist?list=PLZoTAELRMXVOk1pRcOCaG5xtXxgMalpIe

Also implemented the Retraining Pipeline example in retrain_test branch where my data is kept in remote GDrive folder

create env

conda create -n wineq python=3.7 -y

activate env

conda activate wineq

created a req file

install the req

pip install -r requirements.txt

download the data from

https://drive.google.com/drive/folders/18zqQiCJVgF7uzXgfbIJ-04zgz1ItNfF5?usp=sharing

git init

dvc init 

dvc add data_given/winequality.csv

git add .

git commit -m "first commit"

oneliner updates for readme

git add . && git commit -m "update Readme.md"

git remote add origin https://github.com/c17hawke/simple-dvc-demo.git
git branch -M main
git push origin main

tox command -

tox

for rebuilding -

tox -r 

pytest command

pytest -v

setup commands -

pip install -e . 

build your own package commands-

python setup.py sdist bdist_wheel