diff --git a/deepsensor/data/utils.py b/deepsensor/data/utils.py
index c762a077..3970ed14 100644
--- a/deepsensor/data/utils.py
+++ b/deepsensor/data/utils.py
@@ -91,7 +91,7 @@ def compute_xarray_data_resolution(ds: Union[xr.DataArray, xr.Dataset]) -> float
     """
     x1_res = np.abs(np.mean(np.diff(ds["x1"])))
     x2_res = np.abs(np.mean(np.diff(ds["x2"])))
-    data_resolution = np.min([x1_res, x2_res])
+    data_resolution = float(np.min([x1_res, x2_res]))
     return data_resolution
 
 
diff --git a/tests/test_model.py b/tests/test_model.py
index 3e1d1343..3a221207 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -156,7 +156,7 @@ def test_prediction_shapes_lowlevel(self, n_target_sets):
             ):
                 task = tl("2020-01-01", context_sampling, target_sampling)
 
-                n_targets = np.product(expected_obs_shape)
+                n_targets = np.prod(expected_obs_shape)
 
                 # Tensors
                 mean = model.mean(task)
@@ -192,7 +192,7 @@ def test_prediction_shapes_lowlevel(self, n_target_sets):
                     )
 
                 if likelihood in ["cnp", "gnp"]:
-                    n_target_dims = np.product(tl.target_dims)
+                    n_target_dims = np.prod(tl.target_dims)
                     assert_shape(
                         model.covariance(task),
                         (