Thanks for your help. I was able to figure it out from the documentation but adjustments were needed. The model which was based on the Data Bricks auto ML model was really an sk learn pipeline. I have no y values as this is prediction data, not a test set. I needed to use the mlflow sklearn model.
model = mlflow.sklearn.load_model(model_uri)
For Shap tree explainer (shown in the documentation) I needed to use the tree explainer as the model in the pipeline and manually run the other parts of the pipeline. Like this:
explainer = shap.TreeExplainer(model['regressor'])
observations = model["column_selector"].transform(prediction_data)
observations = model["standardizer"].transform(observations)
shap_values = explainer.shap_values(observations)
Another option was not to use the tree explainer: see: https://towardsdatascience.com/using-shap-values-to-explain-how-your-machine-learning-model-works-73...
explainer = shap.Explainer(model.predict, prediction_data)
shap_values = explainer(prediction_data, max_evals = 2000)