After training your machine learning model, deploying it for real-world predictions can be complex.
MLFlow simplifies this by offering a user-friendly interface to deploy models across various platforms without requiring boilerplate code.
With MLflow, the model, code, and configurations are packaged with the deployment container, ensuring consistency between training and deployment environments.
