ML Pipeline with Airflow
Introduction
Now let's build a real ML pipeline in Airflow: data ingestion, preprocessing, training,
evaluation, and conditional deployment. This is the pattern used by production ML systems.
Why This Matters
Individual ML tasks are easy. Orchestrating them reliably is hard:
- Training should only start if data ingestion succeeded
- Deployment should only happen if evaluation passes thresholds
- If training fails, you need alerts and maybe automatic retry
- You need to track which data version trained which model
This pipeline will integrate what you've learned:
- Airflow for orchestration
- MLflow for tracking and registry
- Your FastAPI serving endpoint
After this section, you have an automated ML pipeline that runs on schedule.