Log the statistics of a Pandas dataframe. Note that this method is additive within a run: calling this method with a specific dataset name will not generate a new profile; instead, data will be aggregated into the existing profile.
In order to create a new profile, please specify a dataset_name
df: the Pandas dataframe to log
dataset_name: the name of the dataset (Optional). If not specified, the experiment name is used
Logs a collection of features or a single feature (must specify one or the other).
features: a map of key value feature for model input
feature_name: a dictionary of key->value for multiple features. Each entry represent a single columnar feature
feature_name: name of a single feature. Cannot be specified if 'features' is specified
value: value of as single feature. Cannot be specified if 'features' is specified
dataset_name: the name of the dataset. If not specified, we fall back to using the experiment name
Hijack the mlflow.models.Model.log method and upload the .whylogs.yaml configuration to the model path This will allow us to pick up the configuration later under /opt/ml/model/.whylogs.yaml path
Enable whylogs in
mlflow module via
True if MLFlow has been patched. False otherwise.
.. code-block:: python :caption: Example of whylogs and MLFlow