Apache Airflow (ML Edition)
Orchestrate ML workflows with Apache Airflow. This Action Pack guides you through setting up an Airflow DAG to trigger a simple machine learning task, showcasing Airflow's capabilities in MLOps.
4 Steps
- 1
Install Apache Airflow: Install Airflow using pip. We'll use the 'apache-airflow' package with the 'amazon' extra for AWS integration.
- 2
Configure Airflow: Initialize the Airflow database. This creates the necessary tables for Airflow to operate.
- 3
Create a Simple DAG: Create a DAG file (e.g., `ml_pipeline.py`) in your Airflow DAGs folder. This DAG will define a simple ML task (e.g., printing a message).
- 4
Run the DAG: Unpause the DAG in the Airflow UI and trigger a DAG run. Monitor the task execution in the Airflow UI.
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