End-to-end-ML-project

From Krish Naik course

Workflows in tutorial

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 main.py
9. Update app.py

How to run?

STEPS:

Clone the repository

https://github.com/entbappy/End-to-end-Machine-Learning-Project-with-MLflow

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

dagshub

dagshub

MLFLOW_TRACKING_URI=https://dagshub.com/keenanandrew/End-to-end-ML-project.mlflow
MLFLOW_TRACKING_USERNAME=keenanandrew
MLFLOW_TRACKING_PASSWORD=50949a0af1030805e0f1b0018786b9f0e16b731f
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/keenanandrew/End-to-end-ML-project.mlflow

export MLFLOW_TRACKING_USERNAME=keenanandrew 

export MLFLOW_TRACKING_PASSWORD=50949a0af1030805e0f1b0018786b9f0e16b731f