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 (Jenkins):

$ sudo su
$ docker start jenkins-server
$ docker ps
Get Initial Admin Password run this command in your terminal:
$ docker exec jenkins-server cat /var/jenkins_home/secrets/initialAdminPassword
 
To Access the web interface run: http://YOUR_SERVER_IP:8081

Usage Instructions for ML Pipelines(MLFlow)

$ cps aux | grep mlflow

To Access the web interface of MLFlow run:  http://YOUR_SERVER_IP:8080


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.