Data science pipelines are crucial for transforming raw data into actionable insights through a series of structured processes, ultimately enabling scalable machine learning model deployment in real-world settings. The blog post emphasizes the importance of Machine Learning Operations (MLOps) in ensuring that data science projects move beyond experimentation by establishing automated, robust systems. It highlights Kedro, an open-source Python framework, as a tool that facilitates the creation of reproducible and maintainable data science pipelines by applying software engineering concepts to machine learning code. The article provides a detailed walkthrough on building an anomaly detection pipeline using Kedro, illustrating its modular structure, which includes data engineering, data science, and model evaluation components. Kedro's benefits, such as experiment tracking, pipeline slicing, and simplified project documentation, are also discussed, underscoring its role in overcoming common challenges in transitioning data science projects from development to production. Real-world examples, like NASA and Telkomsel, demonstrate Kedro's effectiveness in enhancing pipeline efficiency and reliability across various industries.