/Ml-Project-with-ml-flow

Explore machine learning project management with MLflow. Learn to efficiently organize, track, and share your experiments and models. Elevate your understanding of ML workflows with this impactful project.

Primary LanguageCSSMIT LicenseMIT

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/Mudit-Sharma-30/Ml-Project-with-ml-flow

STEP 01- Create a conda environment after opening the repository

conda create -n mlproj python=3.8 -y
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/Mudit-Sharma-30/Ml-Project-with-ml-flow.mlflow
MLFLOW_TRACKING_USERNAME=Mudit-Sharma-30
MLFLOW_TRACKING_PASSWORD=3625d590b439d6f0f1c25b63436532ae1dce8180
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/Mudit-Sharma-30/Ml-Project-with-ml-flow.mlflow

export MLFLOW_TRACKING_USERNAME=Mudit-Sharma-30

export MLFLOW_TRACKING_PASSWORD=3625d590b439d6f0f1c25b63436532ae1dce8180

About MLflow

MLflow

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

Deployed Project

http://muditsharma.pythonanywhere.com/