Manual workflows don't scale. Once you have multiple models, data sources, and training schedules, orchestration becomes non-negotiable.
The Orchestration Problem
As your ML system grows, you face several interconnected challenges:
- Data arrives at different times from different sources
- Models need retraining on schedules (daily, weekly, monthly)
- Each stage depends on the previous one succeeding
- Failures cascade through the entire pipeline
- You need to retry failed steps without rerunning everything
Workflow Orchestration Tools
Apache Airflow: Excellent for complex DAGs (directed acyclic graphs). Python-native, great monitoring dashboard. Best if you need complex branching logic.
Kubeflow: Kubernetes-native orchestration. Better for distributed training. Steeper learning curve but powerful for large-scale work.
Prefect/Dagster: Modern alternatives. Cleaner APIs than Airflow. Good for data engineering + ML hybrid workflows.
Pipeline Design Patterns
Data Ingestion → Validation → Feature Engineering → Training → Evaluation → Deployment
Each stage should be:
- Independent (can be updated without changing others)
- Idempotent (running twice produces same result)
- Observable (logs, metrics, alerts)
- Testable (unit tests for data transformations)
Common Mistakes
Coupling stages tightly. If feature engineering changes, you shouldn't need to rewrite the training stage.
Ignoring data quality. Bad data through a perfect pipeline is still bad. Validate early, validate often.
No rollback strategy. How do you revert to the previous model version if the new one performs poorly?
Monitoring and Alerting
- Pipeline completion time (SLA tracking)
- Stage-specific failure rates
- Data drift detection between stages
- Resource usage (CPU, memory, cost)
Key Insight: Your orchestration tool should disappear into the background. If people are constantly fighting the tool instead of building ML systems, you picked wrong.