Summary

In this chapter, we set up a local MLflow development environment that has full support for backend storage and artifact storage using MySQL and the MinIO object store. This will be very useful for us when we develop MLflow-supported DL models in this book. We started by presenting the open provenance tracking framework and asked model provenance tracking questions that are of interest. We worked on addressing the issues of auto-logging and successfully registered a trained model by loading a trained model from a logged model in MLflow for prediction using the mlflow.pytorch.load_model API. We also experimented on how to directly use MLflow's log_metrics, log_params, and log_model APIs without auto-logging, which gives us more control and flexibility over how we can log additional or customized metrics and parameters. We were able to answer many of the provenance questions by performing model provenance tracking, as well as by providing a couple of the questions that require...