The article provides a detailed comparison of three popular machine learning orchestration tools: Kedro, ZenML, and Metaflow, highlighting their capabilities, features, and applications in real-world projects. Each tool has its unique advantages and limitations, with Kedro being praised for its structured framework and data abstraction, making it ideal for complex projects. ZenML is noted for its customizable code structure and ability to work with multiple stacks without code changes, which is advantageous for prototype projects. Meanwhile, Metaflow stands out for its automatic metric tracking and UI capabilities, which are beneficial in production environments, despite its less structured methodology compared to the others. The article also emphasizes the importance of selecting the right tool based on specific project needs and the trade-offs between each tool's capabilities.