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The project aims to develop a machine learning application for predicting loan approval using data from historical loan applications. Leveraging machine learning techniques, the application aims to provide insights into whether a loan application is likely to be approved or rejected based on various factors such as income, credit history, loan amount, etc.
Explain the purpose and goals of your project.
This project utilizes MLflow for experiment tracking, packaging code into reproducible runs, and sharing and deploying models. MLflow helps streamline the machine learning lifecycle.
MLFLOW_TRACKING_URI=https://dagshub.com/naimurborno/Loan_prediction_tracking_using_mlflow.mlflow MLFLOW_TRACKING_USERNAME=naimurborno MLFLOW_TRACKING_PASSWORD=94ebc629914b4f17304744d67eec0b421a8f74d1 \
MLFLOW_TRACKING_URI=https://dagshub.com/naimurborno/Loan_prediction_tracking_using_mlflow.mlflow MLFLOW_TRACKING_USERNAME=naimurborno MLFLOW_TRACKING_PASSWORD=94ebc629914b4f17304744d67eec0b421a8f74d1 python script.py
Login to AWS console.
Create IAM user with AdministratorAccess
Export the credentials in your AWS CLI by running "aws configure"
Create a s3 bucket
Create EC2 machine (Ubuntu) & add Security groups 5000 port
Run the following command on EC2 machine sudo apt update
sudo apt install python3-pip
sudo pip3 install pipenv
sudo pip3 install virtualenv
mkdir mlflow
cd mlflow
pipenv install mlflow
pipenv install awscli
pipenv install boto3
pipenv shell
aws configure
#Finally mlflow server -h 0.0.0.0 --default-artifact-root s3://mlflow-test-23
#open Public IPv4 DNS to the port 5000
#set uri in your local terminal and in your code export MLFLOW_TRACKING_URI=http://ec2-54-147-36-34.compute-1.amazonaws.com:5000/
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Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?