End-to-end-Machine-Learning-Project-with-MLflow

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/CosmicQuant/Machine-learning-project-for-wine-quality

STEP 01- Create a conda environment after opening the repository

conda create -n mlproj python

conda activate mlproj

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_URI=https://dagshub.com/OceanManDiani/Machine-learning-project-for-wine-quality.mlflow MLFLOW_TRACKING_USERNAME=OceanManDiani MLFLOW_TRACKING_PASSWORD=244396299d25722e433d38d75f4c4b30c6886e56 python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/OceanManDiani/Machine-learning-project-for-wine-quality.mlflow

export MLFLOW_TRACKING_USERNAME=OceanManDiani

export MLFLOW_TRACKING_PASSWORD=244396299d25722e433d38d75f4c4b30c6886e56