ML Pipeline Automation

ML pipeline automation orchestrates the entire machine learning workflow from data ingestion through model deployment, ensuring reproducibility, scalability, and reliability.

Pipeline Components

• Data Ingestion: Collecting data from multiple sources
• Data Processing: Cleaning, transformation, feature engineering
• Model Training: Training and hyperparameter tuning
• Validation: Cross-validation and testing
• Deployment: Moving models to production
• Monitoring: Tracking performance metrics

Orchestration Platforms

• Apache Airflow: Workflow scheduling with DAGs
• Kubeflow: Kubernetes-native ML workflows
• Jenkins: CI/CD for ML pipelines
• Prefect: Modern data flow orchestration