ml-pipeline-workflow - End-to-End MLOps Pipeline Orchestration Guide
Provides comprehensive guidance for building end-to-end MLOps pipelines from data preparation to model deployment.
Tags
Updated: 2026-09-18Capabilities
Typical Inputs
Typical Outputs
What this skill does
- Design pipeline architecture
- Prepare feature engineering pipelines
- Orchestrate model training jobs
- Validate model performance metrics
- Automate model deployment strategies
Inputs
- Raw input datasets
- Training configurations
- Pipeline goals and constraints
- DAG orchestration templates
Outputs
- Versioned datasets and features
- Trained model artifacts
- Model validation reports
- Deployed model serving endpoints
- Pipeline execution logs and metrics
Requirements
- Python runtime environment
- Orchestration tools like Airflow or Kubeflow
- Experiment tracking system like MLflow
- Model serving infrastructure