diff --git a/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-dataframe.ipynb b/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-dataframe.ipynb
index 94c70f03..a80ad8c5 100644
--- a/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-dataframe.ipynb
+++ b/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-dataframe.ipynb
@@ -92,15 +92,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "File downloaded and saved to /home/rishic/Code/myforks/spark-rapids-examples/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/data/winequality-red.csv\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
     "\n",
@@ -1008,8 +1000,8 @@
    "source": [
     "This second implementation uses **Spark I/O**.\n",
     "\n",
-    "By this we mean Spark reads the dataset and creates a duplicate of the dataset for each worker (1 partition = 1 duplicate), then maps the tuning task onto each partition.  \n",
-    "In practice, this enables the code to be chained to other Dataframe operations (e.g. ETL stages) without the intermediate step of writing to DBFS, at the cost of memory overhead during duplication.\n"
+    "This means that Spark will read the dataset and create a duplicate of the dataset for each worker (1 partition = 1 duplicate), then map the tuning task onto each partition.  \n",
+    "In practice, this enables the code to be chained to other Dataframe operations (e.g. ETL stages) without the intermediate step of writing to DBFS, at the cost of some overhead during duplication.\n"
    ]
   },
   {
@@ -1019,7 +1011,7 @@
     "### Optuna Task\n",
     "\n",
     "We'll use the same task as before, but instead of reading the dataset from the filepath, the task_udf will be mapped onto the dataframe partition.  \n",
-    "The task_udf will be given an iterator of batches over the partition. See the [mapInPandas docs](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.mapInPandas.html) for more info."
+    "The task_udf will be given an iterator of batches over the partition. See the [mapInPandas docs](https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.DataFrame.mapInPandas.html) for more info on how data is passed in the UDF."
    ]
   },
   {
@@ -1051,7 +1043,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll create a new study for this run using the MySQL database, and define the number of tasks/trials."
+    "Like before, create a new study for this run using the MySQL database and define the number of tasks/trials."
    ]
   },
   {
@@ -1101,7 +1093,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We'll also define the following helper function, which will create *n* duplicates of a dataframe in separate partitions."
+    "We'll also define the following helper function, which will create duplicates of the dataframe held in separate partitions."
    ]
   },
   {
diff --git a/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-joblibspark.ipynb b/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-joblibspark.ipynb
index bf69be32..4cda30e4 100644
--- a/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-joblibspark.ipynb
+++ b/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/optuna-joblibspark.ipynb
@@ -32,8 +32,7 @@
     "# Distributed Hyperparameter Tuning: Optuna + JoblibSpark\n",
     "\n",
     "\n",
-    "This demo demonstrates distributed hyperparameter tuning for XGBoost using the [JoblibSpark backend](https://github.com/joblib/joblib-spark).   \n",
-    "This builds on the [example from Databricks](https://docs.databricks.com/en/machine-learning/automl-hyperparam-tuning/optuna.html).  \n",
+    "This demo demonstrates distributed hyperparameter tuning for XGBoost using the [JoblibSpark backend](https://github.com/joblib/joblib-spark), building on this [example from Databricks](https://docs.databricks.com/en/machine-learning/automl-hyperparam-tuning/optuna.html).  \n",
     "We implement best practices to precompute data and maximize computations on the GPU.  \n",
     "\n",
     "\n",
@@ -92,17 +91,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "File downloaded and saved to /home/rishic/Code/myforks/spark-rapids-examples/examples/ML+DL-Examples/Optuna-Spark/optuna-examples/data/winequality-red.csv\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
     "\n",
@@ -526,19 +517,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "24/12/06 19:47:10 WARN Utils: Your hostname, cb4ae00-lcedt resolves to a loopback address: 127.0.1.1; using 10.110.47.100 instead (on interface eno1)\n",
-      "24/12/06 19:47:10 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address\n",
-      "Setting default log level to \"WARN\".\n",
-      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
-      "24/12/06 19:47:10 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from pyspark.sql import SparkSession\n",
     "from pyspark import SparkConf\n",
@@ -677,7 +656,7 @@
    "source": [
     "Define the number of tasks, number of trials, and trials per task. \n",
     "\n",
-    "**NOTE**: for standalone users running on a single worker, the 4 tasks will all be assigned to the same worker and executed sequentially in this demonstration. However, this can easily be scaled up by adding more workers."
+    "**NOTE**: for standalone users running on a single worker, the 4 tasks will all be assigned to the same worker and executed sequentially in this demonstration. This can easily be scaled up by adding more workers."
    ]
   },
   {
@@ -752,21 +731,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/rishic/anaconda3/envs/optuna-spark/lib/python3.10/site-packages/joblibspark/backend.py:115: UserWarning: Spark version does not support stage-level scheduling.\n",
-      "  warnings.warn(\"Spark version does not support stage-level scheduling.\")\n",
-      "/home/rishic/anaconda3/envs/optuna-spark/lib/python3.10/site-packages/joblibspark/backend.py:154: UserWarning: User-specified n_jobs (4) is greater than the max number of concurrent tasks (1) this cluster can run now.If dynamic allocation is enabled for the cluster, you might see more executors allocated.\n",
-      "  warnings.warn(f\"User-specified n_jobs ({n_jobs}) is greater than the max number of \"\n",
-      "                                                                                \r"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "with joblib.parallel_backend(\"spark\", n_jobs=num_tasks):\n",
     "    results = joblib.Parallel()(\n",
@@ -820,7 +787,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.15"
+   "version": "undefined.undefined.undefined"
   }
  },
  "nbformat": 4,