A machine learning pipeline is a structured framework that automates the stages of building, evaluating, and deploying machine learning models, thereby enhancing efficiency and reducing errors. These pipelines streamline data processing tasks such as normalization and encoding, ensuring reproducibility, consistency, and scalability in handling large data volumes. They also facilitate model evaluation and selection by allowing easy comparison of different algorithms. The use of pipelines allows data scientists to focus on complex tasks like feature engineering and hyperparameter tuning, while simplifying deployment and improving collaboration among team members. Apache Airflow® is highlighted as a versatile open-source tool that enhances the flexibility, reproducibility, and production-readiness of machine learning pipelines by allowing users to build, schedule, and monitor tasks programmatically. It supports both the experimentation and production phases of machine learning, making it easier for data scientists to manage large datasets and integrate machine learning models into business operations efficiently. The article also describes an example pipeline using Airflow that processes Census data to build a classification model, demonstrating the end-to-end process from data extraction to model deployment.