gauravpahwa2021/wine_quality_prediction

An end-to-end machine learning project with MLflow(MLOps Tool for Experiment Tracking and Model Registration) - Wine Quality Predictor

End_To_End_Project_With_MLflow

Wine certification includes physiochemical tests like determination of density, pH, alcohol quantity, fixed and volatile acidity etc. We have a large datasets having the physiochemical tests results and quality on the scale of 1 to 10 of wines of the Vinho Verde variety.Such a model can be used not only by the certification bodies but also by the wine producers to improve quality based on the physicochemical properties and by the consumers to predict the quality of wines.

Workflows

1. Update config.yaml
2. Update schema.yaml
3. Update params.yaml
4. Update the entity
5. Update the configuration manager in src config
6. Update the components
7. Update the pipeline
8. Update the main.py
9. Update the app.py

How to run?

STEPS:

Clone the repository

https://github.com/GauravPahwa2021/End_To_End_Project_With_MLflow

STEP 01- Create a conda environment after opening the repository

conda create -n mlproject python=3.8 -y

conda activate mlproject

STEP 02- install the requirements

pip install -r requirements.txt

# Finally run the following command
python app.py

Now,

open up you local host and port

MLflow

Documentation

cmd

• mlflow ui

dagshub

dagshub

MLFLOW_TRACKING_URL=https://dagshub.com/GauravPahwa2021/End_To_End_Project_With_MLflow.mlflow
MLFLOW_TRACKING_USERNAME=GauravPahwa2021
MLFLOW_TRACKING_PASSWORD=34255e2baf836bf6327d7bb761b9ef93d83d0201
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URL=https://dagshub.com/GauravPahwa2021/End_To_End_Project_With_MLflow.mlflow

export MLFLOW_TRACKING_USERNAME=GauravPahwa2021  

export MLFLOW_TRACKING_PASSWORD=34255e2baf836bf6327d7bb761b9ef93d83d0201

About MLflow

MLflow

• Its Production Grade
• Trace all of your expriements
• Logging & tagging your model