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 :

1. Mlflow :
$sudo su 
$cd /opt/mlops
$source venv/bin/activate
$mlflow --version
$mlflow server \
  --host 0.0.0.0 \
  --port 5000 \
  --allowed-hosts="*" \
  --backend-store-uri sqlite:///mlflow.db
Access  on browser: http://SERVER-IP:5000

2. Kubernetees node :$ kubectl get nodes

3. Argo worlkflow: $kubectl get pods -n argo 

4. Test pipeline :$ python3 train.py

5. Deploy model as API : $ cd /opt/ml-project
source /opt/mlops/venv/bin/activate
uvicorn app:app --host 0.0.0.0 --port 8000

I/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.