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.

CI/CD for ML Pipelines

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