Scalable ML Pipelines with Airflow
DAG-based pipelines, task dependencies, data ingestion → training → evaluation → deploy.
Why Orchestration
An ML workflow has many steps: ingest data, build features, train, evaluate, deploy. Running these by hand is fragile. Apache Airflow orchestrates them as code, with scheduling, retries, dependencies, and monitoring built in.
The DAG
Airflow models a pipeline as a DAG (Directed Acyclic Graph) of tasks. Acyclic means no task can depend on itself in a loop, so execution always has a well-defined order from start to finish.
All lessons in this course
- AI System Architecture Patterns
- Scalable ML Pipelines with Airflow
- Feature Stores: Feast and Tecton
- AI System Observability and Monitoring