MLflow and Experiment Tracking

It replaces version control systems like Git for code management It logs hyperparameters, metrics, artifacts, and code versions for each run, enabling reproducibility, comparison, and organized model management It automatically trains the best model for a given dataset It provides GPU resources for training in the cloud