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Welcome to the Vehicle Insurance Fraud Detection project! 🚗🕵️♂️
Configure Project:
config.yaml
to tailor settings.schema.yaml
for data schema changes.Codebase Setup:
src/config
.Pipeline Enhancements:
Application Integration:
main.py
to incorporate the latest changes.app.py
for seamless app functionality.Clone the repository:
git clone https://github.com/RagalahariAkula-42/vehicle_insurance_fraud_detection
Create Conda Environment:
conda create -n vifd python=3.8 -y
conda activate vifd
Install Requirements:
pip install -r requirements.txt
Run the Application:
python app.py
Open Local Host: Open your local host and port to interact with the application.
For detailed experiment tracking, use MLflow. Start MLflow UI with mlflow ui command.
Utilize DAGshub for enhanced version control. Export environment variables for seamless collaboration:
export MLFLOW_TRACKING_URI=https://dagshub.com/****/******.mlflow
export MLFLOW_TRACKING_USERNAME=****
export MLFLOW_TRACKING_PASSWORD=********
MLflow ensures production-grade model tracking. Log and tag your models for effective monitoring.
#with specific access
1. EC2 access : It is virtual machine
2. ECR: Elastic Container registry to save your docker image in aws
#Description: About the deployment
1. Build docker image of the source code
2. Push your docker image to ECR
3. Launch Your EC2
4. Pull Your image from ECR in EC2
5. Lauch your docker image in EC2
#Policy:
1. AmazonEC2ContainerRegistryFullAccess
2. AmazonEC2FullAccess
- Save the URI: 126391160164.dkr.ecr.ap-northeast-3.amazonaws.com/faceshape
#optinal
sudo apt-get update -y
sudo apt-get upgrade
#required
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker ubuntu
newgrp docker
setting>actions>runner>new self hosted runner> choose os> then run command one by one
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_REGION = us-east-1
AWS_ECR_LOGIN_URI = demo>> 566373416292.dkr.ecr.ap-south-1.amazonaws.com
ECR_REPOSITORY_NAME = simple-app
Feel free to contribute and make our vehicle insurance fraud detection project even better! 🚀👩💻
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Are you sure you want to delete this access key?