diff --git a/doc/source/data/examples/batch_inference_object_detection.ipynb b/doc/source/data/examples/batch_inference_object_detection.ipynb
index 71ad25620694..922bb47a6c7b 100644
--- a/doc/source/data/examples/batch_inference_object_detection.ipynb
+++ b/doc/source/data/examples/batch_inference_object_detection.ipynb
@@ -429,7 +429,7 @@
     "from torchvision.transforms.functional import to_pil_image\n",
     "\n",
     "labels = [weights.meta[\"categories\"][i] for i in prediction[\"labels\"]]\n",
-    "box = draw_bounding_boxes(img, \n",
+    "box = draw_bounding_boxes(img,\n",
     "                          boxes=prediction[\"boxes\"],\n",
     "                          labels=labels,\n",
     "                          colors=\"red\",\n",
@@ -444,7 +444,7 @@
    "source": [
     "## Scaling with Ray Data\n",
     "\n",
-    "Then let's see how to scale the previous example to a large set of images. We will use Ray Data to do batch inference in a distributed fashion, leveraging all the CPU and GPU resources in our cluster.\n",
+    "Then let's see how to scale the previous example to a large set of images. We will use Ray Data to do batch inference in a streaming and distributed fashion, leveraging all the CPU and GPU resources in our cluster.\n",
     "\n",
     "### Loading the Image Dataset\n",
     "\n",
@@ -536,7 +536,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Then we use the {meth}`map <ds.data.Dataset.map>` API to apply the function to the whole dataset. By using Ray Data's map, we can scale out the preprocessing to all the resources in our Ray cluster Note, the `map` method is lazy, it won't perform execution until we start to consume the results."
+    "Then we use the {meth}`map <ds.data.Dataset.map>` API to apply the function to the whole dataset. By using Ray Data's map, we can scale out the preprocessing to all the resources in our Ray cluster. Note, the `map` method is lazy, it won't perform execution until we start to consume the results."
    ]
   },
   {
@@ -944,9 +944,11 @@
    "source": [
     "ds = ds.map_batches(\n",
     "    ObjectDetectionModel,\n",
-    "    concurrency=4, # Use 4 GPUs. Change this number based on the number of GPUs in your cluster.\n",
-    "    batch_size=4, # Use the largest batch size that can fit in GPU memory.\n",
-    "    num_gpus=1,  # Specify 1 GPU per model replica. Remove this if you are doing CPU inference.\n",
+    "    # Use 4 GPUs. Change this number based on the number of GPUs in your cluster.\n",
+    "    concurrency=4,\n",
+    "    batch_size=4,  # Use the largest batch size that can fit in GPU memory.\n",
+    "    # Specify 1 GPU per model replica. Remove this if you are doing CPU inference.\n",
+    "    num_gpus=1,\n",
     ")"
    ]
   },