diff --git a/notebooks/train-yolo11-instance-segmentation-on-custom-dataset.ipynb b/notebooks/train-yolo11-instance-segmentation-on-custom-dataset.ipynb
index afb9a18..93f7dce 100644
--- a/notebooks/train-yolo11-instance-segmentation-on-custom-dataset.ipynb
+++ b/notebooks/train-yolo11-instance-segmentation-on-custom-dataset.ipynb
@@ -917,6 +917,84 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Deploy model on Roboflow\n",
+        "\n",
+        "Once you have finished training your YOLOv11 model, you’ll have a set of trained weights ready for use. These weights will be in the `/runs/segment/train/weights/best.pt` folder of your project. You can upload your model weights to Roboflow Deploy to use your trained weights on our infinitely scalable infrastructure.\n",
+        "\n",
+        "The `.deploy()` function in the [Roboflow pip package](https://docs.roboflow.com/python) now supports uploading YOLOv11 weights."
+      ],
+      "metadata": {
+        "id": "4Z2R1eJiz4ux"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "project.version(dataset.version).deploy(model_type=\"yolov11-seg\", model_path=f\"{HOME}/runs/segment/train/\")"
+      ],
+      "metadata": {
+        "id": "IjZQqXwU0UEo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install inference"
+      ],
+      "metadata": {
+        "id": "pz765jno0Xl3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os, random, cv2\n",
+        "import supervision as sv\n",
+        "import IPython\n",
+        "import inference\n",
+        "\n",
+        "model_id = project.id.split(\"/\")[1] + \"/\" + dataset.version\n",
+        "model = inference.get_model(model_id, userdata.get('ROBOFLOW_API_KEY'))\n",
+        "\n",
+        "# Location of test set images\n",
+        "test_set_loc = dataset.location + \"/test/images/\"\n",
+        "test_images = os.listdir(test_set_loc)\n",
+        "\n",
+        "# Run inference on 4 random test images, or fewer if fewer images are available\n",
+        "for img_name in random.sample(test_images, min(4, len(test_images))):\n",
+        "    print(\"Running inference on \" + img_name)\n",
+        "\n",
+        "    # Load image\n",
+        "    image = cv2.imread(os.path.join(test_set_loc, img_name))\n",
+        "\n",
+        "    # Perform inference\n",
+        "    results = model.infer(image)[0]\n",
+        "    detections = sv.Detections.from_inference(results)\n",
+        "\n",
+        "    # Annotate boxes and labels\n",
+        "    mask_annotator = sv.MaskAnnotator()\n",
+        "    label_annotator = sv.LabelAnnotator()\n",
+        "    annotated_image = mask_annotator.annotate(scene=image, detections=detections)\n",
+        "    annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)\n",
+        "\n",
+        "    # Display annotated image\n",
+        "    _, ret = cv2.imencode('.jpg', annotated_image)\n",
+        "    i = IPython.display.Image(data=ret)\n",
+        "    IPython.display.display(i)\n"
+      ],
+      "metadata": {
+        "id": "seFGjEE20X05"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "source": [
diff --git a/notebooks/train-yolo11-object-detection-on-custom-dataset.ipynb b/notebooks/train-yolo11-object-detection-on-custom-dataset.ipynb
index b46a4e9..1f9b8ce 100644
--- a/notebooks/train-yolo11-object-detection-on-custom-dataset.ipynb
+++ b/notebooks/train-yolo11-object-detection-on-custom-dataset.ipynb
@@ -888,21 +888,23 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "fUFNm4O0znB2"
+      },
       "source": [
         "## Deploy model on Roboflow\n",
         "\n",
         "Once you have finished training your YOLOv11 model, you’ll have a set of trained weights ready for use. These weights will be in the `/runs/detect/train/weights/best.pt` folder of your project. You can upload your model weights to Roboflow Deploy to use your trained weights on our infinitely scalable infrastructure.\n",
         "\n",
-        "The `.deploy()` function in the [Roboflow pip package](https://docs.roboflow.com/python) now supports uploading YOLOv11 weights.\n",
-        "\n",
-        "To upload model weights, add the following code to the “Inference with Custom Model” section in the aforementioned notebook:"
+        "The `.deploy()` function in the [Roboflow pip package](https://docs.roboflow.com/python) now supports uploading YOLOv11 weights."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "3Y6_K1MRznB2"
+      },
       "outputs": [],
       "source": [
         "project.version(dataset.version).deploy(model_type=\"yolov11\", model_path=f\"{HOME}/runs/detect/train/\")"
@@ -911,7 +913,9 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "V5K2IU_SznB2"
+      },
       "outputs": [],
       "source": [
         "!pip install inference"
@@ -920,7 +924,9 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "BwYVU9S5znB2"
+      },
       "outputs": [],
       "source": [
         "import os, random, cv2\n",
@@ -938,20 +944,20 @@
         "# Run inference on 4 random test images, or fewer if fewer images are available\n",
         "for img_name in random.sample(test_images, min(4, len(test_images))):\n",
         "    print(\"Running inference on \" + img_name)\n",
-        "    \n",
+        "\n",
         "    # Load image\n",
         "    image = cv2.imread(os.path.join(test_set_loc, img_name))\n",
-        "    \n",
+        "\n",
         "    # Perform inference\n",
         "    results = model.infer(image, confidence=0.4, overlap=30)[0]\n",
         "    detections = sv.Detections.from_inference(results)\n",
-        "    \n",
+        "\n",
         "    # Annotate boxes and labels\n",
         "    box_annotator = sv.BoxAnnotator()\n",
         "    label_annotator = sv.LabelAnnotator()\n",
         "    annotated_image = box_annotator.annotate(scene=image, detections=detections)\n",
         "    annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections)\n",
-        "    \n",
+        "\n",
         "    # Display annotated image\n",
         "    _, ret = cv2.imencode('.jpg', annotated_image)\n",
         "    i = IPython.display.Image(data=ret)\n",
@@ -1011,4 +1017,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}
\ No newline at end of file