Welcome to the third lesson in our "Automating Retraining with Apache Airflow" course! In our previous lessons, we introduced Apache Airflow's core concepts and discussed how to design a ML pipeline DAG using the TaskFlow API. Now that we have our machine learning pipeline designed, we need to understand how to test and run it effectively.

Testing is a critical part of DAG development. Before scheduling a workflow to run in production, you need to validate that each task functions correctly and that the DAG as a whole behaves as expected. Fortunately, Apache Airflow provides a robust Command Line Interface (CLI) that makes testing DAGs and individual tasks straightforward. In this lesson, you'll learn essential Airflow CLI commands to verify your ML pipeline works correctly — from listing available DAGs to testing specific tasks. These skills will help you build confidence in your pipelines before deploying them to production environments.

The Airflow Command Line Interface (CLI) is your direct channel to interact with Airflow from the terminal. Think of it as a powerful toolkit that lets you manage and test your workflows without relying on the web interface.

When developing ML pipelines, the CLI becomes an essential part of your workflow for several reasons:

• Development and debugging: You can quickly test DAGs and tasks during development without waiting for scheduled runs.
• CI/CD pipelines: You can automate DAG validation in continuous integration workflows.
• Troubleshooting: You can investigate issues in your workflows with detailed logging.

The CLI is particularly valuable in machine learning contexts where errors can be costly. Imagine deploying a model that was trained on corrupted data or with improper hyperparameters — this could lead to poor predictions that impact business decisions. By using the CLI to thoroughly test each component of your pipeline, you can catch these issues early and ensure your automated retraining systems maintain model quality over time.

Before you can test specific DAGs, you need to know what's available in your environment. Let's look at the commands for listing and inspecting DAGs:

When you run this command, Airflow displays a tabular list of all DAGs, including crucial information like the DAG ID, schedule interval, and whether each DAG is paused. This gives you a bird's-eye view of all workflows in your system. Remember that DAGs code must be correctly saved in $AIRFLOW_HOME/dags to enable discovery!

Once you've identified your ML pipeline DAG (in our case, mlops_pipeline), you can examine its details:

This command reveals the architecture of your DAG, showing all tasks and their connections. It's especially useful when you want to verify the structure of a complex workflow without accessing the web UI. You can think of it as an "X-ray" for your pipeline, letting you confirm that all components are connected correctly before execution.

Now that you can see your DAG, you'll want to verify that it runs correctly end-to-end. The following command is your go-to tool for this purpose:

When you run this command, Airflow executes your entire ML pipeline in sequence — from data extraction to model deployment. Unlike scheduled runs, this test execution:

• Runs immediately rather than waiting for the next scheduled interval.
• Shows all logs directly in your console in real-time.
• Doesn't record the run in Airflow's database, keeping your metadata clean.

This is like a dry run of your pipeline, letting you observe every step without affecting your production environment. It's invaluable when you've made changes to your DAG and want to ensure everything still works together smoothly before committing to regular execution.

Sometimes you'll need to focus on specific parts of your ML pipeline. For example, you might want to test just the model training task after changing your algorithm. The CLI has you covered:

This command reveals all the individual components in your pipeline, which is useful when you need to remember exact task IDs for further testing. Once you've identified the task you want to focus on, you can test it individually:

The date parameter (2023-09-15 in this example) provides context for your task, which can be important if your task behavior depends on the execution date. This focused testing approach is like using a microscope on your pipeline, allowing you to inspect and debug individual components without running the entire workflow.

When testing ML pipelines in Airflow, you'll likely encounter some challenges. Here are solutions to common issues you might face:

Task dependencies not working correctly? Verify how you've defined relationships in your DAG. With the TaskFlow API, dependencies are created when you pass the result of one task to another. For example, in our pipeline, the train_model task takes the output from transform_data as its input, creating an automatic dependency.

Tasks failing unexpectedly? Isolate the issue by testing just the problematic task. The detailed logs will often reveal what's going wrong:

When troubleshooting ML pipelines specifically, pay close attention to data quality issues, resource constraints (ML training often needs significant memory), and model performance thresholds that might be triggering failures. These domain-specific concerns often require different debugging approaches than standard data pipelines.

In this lesson, you've learned how to leverage the Airflow CLI to thoroughly test your ML pipelines. We've covered commands for exploring DAGs, testing entire workflows, and validating individual tasks. These tools are essential for ensuring your automated retraining systems work correctly before deploying them to production.

The next part of our course will build on this foundation by exploring more advanced Airflow features for ML pipelines, including strategies for handling large datasets, optimizing resource usage during training, and implementing sophisticated model validation logic. With these skills, you'll be able to create robust, production-quality ML pipelines that reliably deliver high-performing models.