Apache Airflow is highlighted as a powerful tool for orchestrating machine learning pipelines, providing robust solutions for dependency management, scalability, error handling, and extensibility in managing complex ML workflows. It facilitates a seamless producer/consumer relationship by ensuring that consumer tasks are executed only after producer tasks have completed, thereby preventing data inconsistencies and delays. This is exemplified by a practical implementation where a producer DAG extracts and loads data, triggering a consumer DAG to train and run predictive models, allowing teams to operate independently while maintaining data integrity. Airflow's ability to handle large datasets through parallel execution, combined with its detailed monitoring and logging capabilities, makes it a vital component of a modern MLOps platform, enabling organizations to optimize their machine learning operations and enhance collaboration between teams.