diff --git a/ludwig/backend/ray.py b/ludwig/backend/ray.py
index ab238208272..d8076b74d51 100644
--- a/ludwig/backend/ray.py
+++ b/ludwig/backend/ray.py
@@ -812,17 +812,24 @@ def initialize(self):
         # Disable placement groups on dask
         dask.config.set(annotations={"ray_remote_args": {"placement_group": None}})
 
+    def generate_bundles(self, num_cpu):
+        # Ray requires that each bundle be scheduleable on a single node.
+        # So a bundle of 320 cpus would never get scheduled. For now a simple heuristic
+        # to be used is to just request 1 cpu at a time.
+        return [{"CPU": 1} for _ in range(int(num_cpu))]
+
     @contextlib.contextmanager
     def provision_preprocessing_workers(self):
-        if not self._preprocessor_kwargs.get("use_preprocessing_placement_group", False):
-            logger.warning(
-                "Backend config has use_preprocessing_placement_group set to False or did not set it at all."
-                " provision_preprocessing_workers() is a no-op in this case."
+        num_cpu = self._preprocessor_kwargs.get("num_cpu")
+        if not num_cpu:
+            logger.info(
+                "Backend config has num_cpu not set." " provision_preprocessing_workers() is a no-op in this case."
             )
             yield
         else:
-            num_cpu = self._preprocessor_kwargs["num_cpu_workers"]
-            self._preprocessor_pg = placement_group([{"CPU": num_cpu}])
+            bundles = self.generate_bundles(num_cpu)
+            logger.info("Requesting bundles of %s for preprocessing", bundles)
+            self._preprocessor_pg = placement_group(bundles)
             ready = self._preprocessor_pg.wait(FIFTEEN_MINS_IN_S)
 
             if not ready:
@@ -840,12 +847,6 @@ def provision_preprocessing_workers(self):
                 self._release_preprocessing_workers()
 
     def _release_preprocessing_workers(self):
-        if not self._preprocessor_kwargs.get("use_preprocessing_placement_group", False):
-            logger.warning(
-                "Backend config has use_preprocessing_placement_group set to False or did not set it at all."
-                " _release_preprocessing_workers() is a no-op in this case."
-            )
-            return
         if self._preprocessor_pg is not None:
             remove_placement_group(self._preprocessor_pg)
         self._preprocessor_pg = None
diff --git a/tests/integration_tests/test_ray.py b/tests/integration_tests/test_ray.py
index 2fad952ac62..a8ee5aa8742 100644
--- a/tests/integration_tests/test_ray.py
+++ b/tests/integration_tests/test_ray.py
@@ -631,3 +631,44 @@ def test_ray_distributed_predict(tmpdir, ray_cluster_2cpu):
         # compute the predictions
         preds = preds.compute()
         assert preds.iloc[1].name != preds.iloc[42].name
+
+
+@pytest.mark.distributed
+def test_ray_preprocessing_placement_group(tmpdir, ray_cluster_2cpu):
+    preprocessing_params = {
+        "audio_file_length_limit_in_s": 3.0,
+        "missing_value_strategy": BFILL,
+        "in_memory": True,
+        "padding_value": 0,
+        "norm": "per_file",
+        "type": "fbank",
+        "window_length_in_s": 0.04,
+        "window_shift_in_s": 0.02,
+        "num_filter_bands": 80,
+    }
+    audio_dest_folder = os.path.join(tmpdir, "generated_audio")
+    input_features = [audio_feature(folder=audio_dest_folder, preprocessing=preprocessing_params)]
+    output_features = [binary_feature()]
+
+    config = {
+        "input_features": input_features,
+        "output_features": output_features,
+        TRAINER: {"epochs": 2, "batch_size": 8},
+    }
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        backend_config = {**RAY_BACKEND_CONFIG}
+        backend_config["preprocessor_kwargs"] = {"num_cpu": 1}
+        csv_filename = os.path.join(tmpdir, "dataset.csv")
+        dataset_csv = generate_data(input_features, output_features, csv_filename, num_examples=100)
+        dataset = create_data_set_to_use("csv", dataset_csv, nan_percent=0.0)
+        model = LudwigModel(config, backend=backend_config)
+        _, _, output_dir = model.train(
+            dataset=dataset,
+            training_set=dataset,
+            skip_save_processed_input=True,
+            skip_save_progress=True,
+            skip_save_unprocessed_output=True,
+            skip_save_log=True,
+        )
+        preds, _ = model.predict(dataset=dataset)