CI/CD for ML Pipelines

CI/CD for ML Pipelines is an automated framework designed to streamline the development, testing, deployment, and monitoring of machine learning models. It provides a unified, collaborative environment that enables teams to build, validate, and deploy ML workflows efficiently, ensuring reproducibility, scalability, and faster delivery of AI solutions.

Features of CI/CD for ML Pipelines:

• Automated model training, testing, and deployment using CI/CD tools like Jenkins, GitHub Actions, or GitLab CI.
• Integration with ML frameworks and tools such as MLflow, DVC, TensorFlow, or PyTorch.
• Collaborative pipelines with version control for code, data, and models.
• Scalable orchestration using Docker, Kubernetes, or cloud-native services.
• Monitoring, logging, and workflow automation for continuous improvement of ML models.

Usage Instruction for CI/CD :

$sudo su 
$cd /opt
$cd ~/mlops-pipeline
$docker run -d --name jenkins-server   -p 8081:8080 -p 50000:50000   -v jenkins_home:/var/jenkins_home   -v /var/run/docker.sock:/var/run/docker.sock   jenkins/jenkins:lts
$docker run -d --name mlflow-server   -p 8080:5000   ghcr.io/mlflow/mlflow mlflow server   --backend-store-uri sqlite:///mlflow.db   --default-artifact-root /mlflow/artifacts   --host 0.0.0.0   --allowed-hosts "Your-Server-IP:8080,localhost:8080"
$docker ps --format "table {{.Names}}\t{{.Status}}\t{{.Ports}}"
$source ~/mlops-env/bin/activate
$mlflow --version
$python test_pipeline.py

# Get Login Credentials:
$ cat /var/jenkins/credentials.txt

Access MLflow on browser: http://SERVER-IP:8080
Access Jenkins on browser: http://SERVER-IP:8081

Disclaimer:

CI/CD for ML pipelines requires proper setup and configuration of the underlying tools and infrastructure. Users are responsible for configuring pipelines, access controls, compute resources, and data security. While CI/CD simplifies ML workflow automation and deployment, proper governance and monitoring are essential for reliable and secure ML operations.