This guide serves as a practical introduction to deploying machine learning models as APIs, targeting developers familiar with Python and Scikit-learn. The methodology is sound for a basic workflow, though it lacks depth in areas like model validation, hyperparameter tuning, and production-grade error handling. The use of a built-in dataset simplifies the example but may not reflect real-world data complexities, such as missing values or class imbalance. The deployment process is straightforward...