project=client.get_project("EXISTING_PROJECT_KEY")to load the existing project, then use:upload_model to upload a new version of the model you want to inspect/testupload_dataset to upload a new dataset that you want to apply to your existing modelprediction_functionmodel_type=classificationmodel_type=regressionmodel_typedfdf can contain more columns than the features of the model such as the actual ground truth variable, sample_id, metadata, etc. df should be raw data that comes before all the preprocessing stepscolumn_typesnumeric, category or text) for all columns of df. targetdf corresponding to the actual target variable (ground truth).feature_namesprediction_function. By default, feature_namesare all the keys from column_types except from target. Some important remarks:prediction_function(df[feature_names]) does not return an error messageclassification_labelsprediction_task="classification". Some important remarks:classification_labels is a list of n elements, make sure prediction_function is also returning n probabilitiesprediction_functionclassification_thresholdmodel_namedataset_nameprediction_function as a function that wraps the whole prediction pipeline: all the preprocessing steps (categorical encoding, etc.) + ML prediction. This is key for a robust and interpretable inspection stage! Click here for examples with and without pipelines.prediction_function(df[feature_names]) gets executed without an error. This is the only requirement to upload a model on Giskard!