Add link model deployment to example notebook (#904)

* add link model deployment

* add pymc-marketing to watermark

docs/source/guide/benefits/model_deployment.ipynb

@@ -5,6 +5,7 @@
    "id": "7fb27b941602401d91542211134fc71a",
    "metadata": {},
    "source": [
+    "(model_deployment)=\n",
     "# Model deployment"
    ]
   },

docs/source/notebooks/mmm/mmm_example.ipynb

@@ -10302,7 +10302,7 @@
    "metadata": {},
    "source": [
     "## 10. Save Model\n",
-    "After your model is train, you can quickly save it using the `save` method."
+    "After your model is train, you can quickly save it using the `save` method. For more information about model deployment see {ref}`model_deployment`."
    ]
   },
   {
@@ -10316,27 +10316,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: Thu Jun 27 2024\n",
+      "The watermark extension is already loaded. To reload it, use:\n",
+      "  %reload_ext watermark\n",
+      "Last updated: Fri Aug 02 2024\n",
       "\n",
       "Python implementation: CPython\n",
       "Python version       : 3.12.4\n",
       "IPython version      : 8.25.0\n",
       "\n",
-      "pytensor: 2.22.1\n",
+      "pymc_marketing: 0.8.0\n",
+      "pytensor      : 2.22.1\n",
       "\n",
-      "matplotlib: 3.9.0\n",
+      "numpy     : 1.26.4\n",
       "seaborn   : 0.13.2\n",
+      "matplotlib: 3.9.0\n",
+      "pymc      : 5.15.1\n",
       "arviz     : 0.18.0\n",
-      "numpy     : 1.26.4\n",
       "pandas    : 2.2.2\n",
-      "pymc      : 5.15.1\n",
       "\n",
       "Watermark: 2.4.3\n",
       "\n"
@@ -10345,7 +10348,7 @@
    ],
    "source": [
     "%load_ext watermark\n",
-    "%watermark -n -u -v -iv -w -p pytensor"
+    "%watermark -n -u -v -iv -w -p pymc_marketing,pytensor"
    ]
   }
  ],