Thanks, @datajoely. Could you elaborate more on different parts of the run lifecycle that could possibly use hooks? I am looking at this article https://medium.com/quantumblack/introducing-kedro-hooks-fd5bc4c03ff5, and it uses Mlflow as an example, and there are two

if

statements in the scripts.

if node._func_name == "split_data":
            mlflow.log_params(
                {"split_data_ratio": inputs["params:example_test_data_ratio"]}
            )

        elif node._func_name == "train_model":
            model = outputs["example_model"]
            mlflow.sklearn.log_model(model, "model")
            mlflow.log_params(inputs["parameters"])

These are node specific functions, Do you think these are better put in the node logic itself instead of hooks or it is a proper user case for hooks? Or as @noklam suggested using kedro-mlflow?