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/shubhamgore2468/MLOPS

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/shubhamgore2468/MLOPS.mlflow
MLFLOW_TRACKING_USERNAME=shubhamgore2468
MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/shubhamgore2468/MLOPS.mlflow

export MLFLOW_TRACKING_USERNAME=shubhamgore2468

export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0