rrizwan98/end_to_end_ml_project_with_mlflow_and_deployment

𝗠𝗟 𝗽𝗿𝗼𝗷𝗲𝗰𝘁, encompassing key topics like 𝗗𝗮𝗴𝘀𝗵𝘂b and 𝗠𝗟𝗳𝗹𝗼𝘄 for version control, 𝗠𝗟𝗢𝗽𝘀 practices for efficient deployment, and robust 𝗖𝗜/𝗖𝗗 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 setup. Showcased 𝗔𝗪𝗦 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 with the help of 𝗚𝗶𝘁𝗛𝘂𝗯 𝗔𝗰𝘁𝗶𝗼𝗻 prowess for seamless machine learning application integration.

End_to_End_ML_Project_with_mlflow_and_Deployment

How to run?

STEPS:

Clone the repository

https://github.com/rrizwan98/End_to_End_ML_Project_with_mlflow_and_Deployment.git

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/rrizwan98/End_to_End_ML_Project_with_mlflow_and_Deployment.mlflow
MLFLOW_TRACKING_USERNAME=rrizwan98
MLFLOW_TRACKING_PASSWORD=d685b0bd147ef96de518be68b7d128d4d70ca84f
python script.py

Run this to export as env variables:

export MLFLOW_TRACKING_URI=https://dagshub.com/rrizwan98/End_to_End_ML_Project_with_mlflow_and_Deployment.mlflow

export MLFLOW_TRACKING_USERNAME=rrizwan98 

export MLFLOW_TRACKING_PASSWORD=d685b0bd147ef96de518be68b7d128d4d70ca84f