MLFlow's "projects" module: Helps in generating the best model by packaging data science code, ensuring reproducibility across various platforms.
Packaging: Can be likened to creating an image, similar to Docker, where an OS environment is set up with necessary dependencies, and the actual code runs on top.
Execution: Projects can run both locally or remotely, supporting various platforms including Docker, Azure, and other cloud platforms.
Configuration Details: A project includes the data, dependencies, and Python code to be executed, ensuring everything is packaged correctly.
Dependency Management: By default, MLFlow captures dependencies using Conda Channel. For machine learning deployments using mlc, Conda is preferred, while Docker can be used for non-Python packages and system packages.

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