An MLflow project offers a standardized way to package data science code, ensuring reproducibility and reusability. It can be defined by structuring a directory or Git repository following specific conventions or using a YAML file for additional control. Projects can be run manually or automatically, from the command line or through code, and either standalone or within workflows. ^14b9d4 Key properties of a project include its name, entry points, and environment. The name is a human-readable identifier for the project. Entry points are the commands executable within the project. The environment defines the required software setup to run the project. ^5d62c6