ML Pipeline Automation
Master ML pipeline automation, CI/CD for ML, and automated training workflows. This is a foundational concept in artificial intelligence and machine learning that professional developers rely on daily. The explanations below are written to be beginner-friendly while covering the depth and nuance that comes from real-world AI/ML experience. Take your time with each section and practice the examples
45 min•By Priygop Team•Last updated: Feb 2026
ML Pipeline Components
- Data Ingestion: Automated data collection and validation
- Feature Engineering: Automated feature creation and selection
- Model Training: Automated model training and validation
- Model Deployment: Automated model deployment and serving
CI/CD for ML
- Continuous Integration: Automated testing and validation
- Continuous Deployment: Automated model deployment
- Continuous Training: Automated model retraining
- Continuous Monitoring: Automated model performance monitoring
Pipeline orchestration
- Apache Airflow: Workflow orchestration platform
- Kubeflow: Kubernetes-based ML platform
- Prefect: Modern workflow orchestration
- Argo Workflows: Cloud-native workflow engine
Automation Tools
- GitHub Actions: CI/CD for ML projects
- Jenkins: Build automation and deployment
- CircleCI: Cloud-based CI/CD platform
- GitLab CI: Integrated CI/CD pipeline