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๐— ๐—Ÿ ๐—ฝ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜, encompassing key topics like ๐——๐—ฎ๐—ด๐˜€๐—ต๐˜‚b and ๐— ๐—Ÿ๐—ณ๐—น๐—ผ๐˜„ for version control, ๐— ๐—Ÿ๐—ข๐—ฝ๐˜€ practices for efficient deployment, and robust ๐—–๐—œ/๐—–๐—— ๐—ฝ๐—ถ๐—ฝ๐—ฒ๐—น๐—ถ๐—ป๐—ฒ setup. Showcased ๐—”๐—ช๐—ฆ ๐—ฑ๐—ฒ๐—ฝ๐—น๐—ผ๐˜†๐—บ๐—ฒ๐—ป๐˜ with the help of ๐—š๐—ถ๐˜๐—›๐˜‚๐—ฏ ๐—”๐—ฐ๐˜๐—ถ๐—ผ๐—ป prowess for seamless machine learning application integration.

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rrizwan98/End_to_End_ML_Project_with_mlflow_and_Deployment

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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

About

๐— ๐—Ÿ ๐—ฝ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜, encompassing key topics like ๐——๐—ฎ๐—ด๐˜€๐—ต๐˜‚b and ๐— ๐—Ÿ๐—ณ๐—น๐—ผ๐˜„ for version control, ๐— ๐—Ÿ๐—ข๐—ฝ๐˜€ practices for efficient deployment, and robust ๐—–๐—œ/๐—–๐—— ๐—ฝ๐—ถ๐—ฝ๐—ฒ๐—น๐—ถ๐—ป๐—ฒ setup. Showcased ๐—”๐—ช๐—ฆ ๐—ฑ๐—ฒ๐—ฝ๐—น๐—ผ๐˜†๐—บ๐—ฒ๐—ป๐˜ with the help of ๐—š๐—ถ๐˜๐—›๐˜‚๐—ฏ ๐—”๐—ฐ๐˜๐—ถ๐—ผ๐—ป prowess for seamless machine learning application integration.

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