diff --git a/.github/workflows/python-CI.yml b/.github/workflows/python-CI.yml
index e05eafe89a..1855289fe9 100644
--- a/.github/workflows/python-CI.yml
+++ b/.github/workflows/python-CI.yml
@@ -7,6 +7,7 @@ on:
         paths:
             - "src/**"
             - "tests/**"
+            - "tutorials/**"
             - "pyproject.toml"
     # Allows you to run this workflow manually from the Actions tab
     workflow_dispatch:
diff --git a/tutorials/example_datasets.ipynb b/tutorials/example_datasets.ipynb
index 623e599a57..dea49e6983 100644
--- a/tutorials/example_datasets.ipynb
+++ b/tutorials/example_datasets.ipynb
@@ -63,7 +63,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "px.load_example?"
+    "?px.load_example"
    ]
   },
   {
diff --git a/tutorials/image_classification_tutorial.ipynb b/tutorials/image_classification_tutorial.ipynb
index 40b6802fe0..4039773c06 100644
--- a/tutorials/image_classification_tutorial.ipynb
+++ b/tutorials/image_classification_tutorial.ipynb
@@ -156,38 +156,11 @@
     "display_examples(train_df.head())"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0BIeGAemfziv"
-   },
-   "source": [
-    "## 3. Prepare the Data\n",
-    "\n",
-    "The original data is from April 2022. Update the timestamps to the current time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "xzYoV-hemYsE"
-   },
-   "outputs": [],
-   "source": [
-    "latest_timestamp = max(prod_df[\"prediction_ts\"])\n",
-    "current_timestamp = datetime.timestamp(datetime.now())\n",
-    "delta = current_timestamp - latest_timestamp\n",
-    "\n",
-    "train_df[\"prediction_ts\"] = (train_df[\"prediction_ts\"] + delta).astype(float)\n",
-    "prod_df[\"prediction_ts\"] = (prod_df[\"prediction_ts\"] + delta).astype(float)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 4. Launch Phoenix\n",
+    "## 3. Launch Phoenix\n",
     "\n",
     "### a) Define Your Schema\n",
     "To launch Phoenix with your data, you first need to define a schema that tells Phoenix which columns of your DataFrames correspond to features, predictions, actuals (i.e., ground truth), embeddings, etc.\n",
@@ -306,7 +279,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 5. Find and Export Problematic Clusters\n",
+    "## 4. Find and Export Problematic Clusters\n",
     "\n",
     "### Steps\n",
     "\n",
@@ -330,7 +303,7 @@
     "1. It consists almost entirely of production data, meaning that your model is seeing data in production the likes of which it never saw during training.\n",
     "1. Your model was trained crisp and high-resolution images. In production, your model is encountering blurry and noisy images that it cannot correctly classify.\n",
     "\n",
-    "## 6. Load and View Exported Data\n",
+    "## 5. Load and View Exported Data\n",
     "\n",
     "View your exported files."
    ]
@@ -385,7 +358,7 @@
    "source": [
     "Congrats! You've pinpointed the blurry or noisy images that are hurting your model's performance in production. As an actionable next step, you can label your exported production data and fine-tune your model to improve performance.\n",
     "\n",
-    "## 7. Close the App\n",
+    "## 6. Close the App\n",
     "\n",
     "When you're done, don't forget to close the app."
    ]
diff --git a/tutorials/sentiment_classification_tutorial.ipynb b/tutorials/sentiment_classification_tutorial.ipynb
index a9c0afab57..f08ab0dfd1 100644
--- a/tutorials/sentiment_classification_tutorial.ipynb
+++ b/tutorials/sentiment_classification_tutorial.ipynb
@@ -246,7 +246,7 @@
     "\n",
     "Congrats! You've identified the root cause of your model's performance issue.\n",
     "\n",
-    "## 5. Close the App 🧹\n",
+    "## 5. Close the App\n",
     "\n",
     "When you're done, don't forget to close the app."
    ]