Further reading

To learn more about the topics that were covered in this chapter, take a look at the following resources:

• MLflow Docker setup reference: https://github.com/sachua/mlflow-docker-compose
• MLflow PyTorch autologging implementation: https://github.com/mlflow/mlflow/blob/master/mlflow/pytorch/_pytorch_autolog.py
• MLflow PyTorch model logging, loading, and registry documentation: https://www.mlflow.org/docs/latest/python_api/mlflow.pytorch.html
• MLflow parameters and metrics logging documentation: https://www.mlflow.org/docs/latest/python_api/mlflow.html
• MLflow model registry documentation: https://www.mlflow.org/docs/latest/model-registry.html
• Digging into big provenance (with SPADE): https://queue.acm.org/detail.cfm?id=3476885
• How to utilize torchmetrics and lightning-flash: https://www.exxactcorp.com/blog/Deep-Learning/advanced-pytorch-lightning-using-torchmetrics-and-lightning-flash
• Why are precision, recall, and F1 score equal when using...