Airflow Fundamentals
Introduction
Apache Airflow is a workflow orchestration platform. You define tasks and their
dependencies as code (DAGs — Directed Acyclic Graphs), and Airflow handles scheduling,
execution, retries, and monitoring.
In ML, Airflow orchestrates pipelines: data ingestion → preprocessing → training →
evaluation → deployment, running on schedule or in response to triggers.
Why This Matters
ML systems aren't one-off scripts — they're pipelines that run repeatedly:
- Daily data ingestion from various sources
- Weekly model retraining on fresh data
- Automated evaluation and promotion
- Scheduled batch inference jobs
Without orchestration, you end up with cron jobs that fail silently, no visibility
into what ran when, and manual intervention for retries.
Airflow provides:
- Visual pipeline representation
- Scheduling and triggering
- Retry logic and failure handling
- Logs and monitoring
- Task dependencies
Tasks
Hint: pip install apache-airflow or docker-compose with official image