---

• Parameters: key-value input to the code (learning rate, what loss function is used, number of filters to use, depth of the tree)
• Metrics: numeric values
• Tags and Notes: information about a run (free text)
• Artifacts: files, data, model
• Source: what code ran?
• Version: which version of the code?
• Run: an instance of code
• Experiment: several Runs

with mlflow.start_run():
    mlflow.log_param("name", value)
    mlflow.log_param(dict)
    ...
    mlflow.log_metric("name", value)
    ...
    mlflow.sklearn.log_model(model)

mlflow ui