diff --git a/source/examples/xgboost-rf-gpu-cpu-benchmark/notebook.ipynb b/source/examples/xgboost-rf-gpu-cpu-benchmark/notebook.ipynb
index 0823ac3d..944dadd3 100644
--- a/source/examples/xgboost-rf-gpu-cpu-benchmark/notebook.ipynb
+++ b/source/examples/xgboost-rf-gpu-cpu-benchmark/notebook.ipynb
@@ -3,7 +3,21 @@
   {
    "cell_type": "markdown",
    "id": "a51c95d1-b447-4f1b-9571-cf597ca93ef4",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "cloud/aws/ec2",
+     "data-storage/s3",
+     "workflow/randomforest",
+     "workflow/hpo",
+     "workflow/xgboost",
+     "library/dask",
+     "library/dask-cuda",
+     "library/xgboost",
+     "library/optuna",
+     "library/sklearn",
+     "library/dask-ml"
+    ]
+   },
    "source": [
     "# HPO Benchmarking with RAPIDS and Dask\n",
     "\n",
@@ -11,7 +25,7 @@
     "\n",
     "In the notebook demo below, we compare benchmarking results to show how GPU can accelerate HPO tuning jobs relative to CPU.\n",
     "\n",
-    "For instance, we find a 26x speedup in wall clock time (0.71 hrs vs. 18.9 hrs) when comparing between GPU and CPU EC2 instances on 100 XGBoost HPO trials using no parallel workers on 3 years of the Airline Dataset.\n"
+    "For instance, we find a 48x speedup in wall clock time (0.71 hrs vs 34.6 hrs) for XGBoost and 16x (3.86 hrs vs 63.2 hrs) for RandomForest when comparing between GPU and CPU EC2 instances on 100 HPO trials on 3 years of the Airline Dataset.\n"
    ]
   },
   {