MLflow is a popular MLOps tool to manage a machine learning lifecycle but lacked support for Autologging parameters, metrics, and artifacts related to a PyTorch deep learning workflow. We worked closely with the Facebook AI Research (FAIR) team to add Autologging and provided support to load the native TorchScript models. We thereby enabled the saving of extra files and additional artifacts. These features were released in MLFlow version 1.12.0.

Contributors
Karthik Sundararaman, Shrinath Suresh, Ankan Ghosh, Sampath Kumaran Ganesan

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