diff --git a/notebooks/search/03-ELSER.ipynb b/notebooks/search/03-ELSER.ipynb
index ddb2a6f6..33ba42d9 100644
--- a/notebooks/search/03-ELSER.ipynb
+++ b/notebooks/search/03-ELSER.ipynb
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -173,9 +173,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model deleted successfully, We will proceed with creating one\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/_8/2nxt7jjx27bd8bm5lw63ht340000gn/T/ipykernel_34494/2089429255.py:3: ElasticsearchWarning: The default [remove_binary] value of 'false' is deprecated and will be set to 'true' in a future release. Set [remove_binary] explicitly to 'true' or 'false' to ensure no behavior change.\n",
+      "  client.ml.delete_trained_model(model_id=\".elser_model_2\",force=True)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ObjectApiResponse({'model_id': '.elser_model_2', 'model_type': 'pytorch', 'model_package': {'packaged_model_id': 'elser_model_2', 'model_repository': 'https://ml-models.elastic.co', 'minimum_version': '11.0.0', 'size': 438123914, 'sha256': '2e0450a1c598221a919917cbb05d8672aed6c613c028008fedcd696462c81af0', 'metadata': {}, 'tags': [], 'vocabulary_file': 'elser_model_2.vocab.json'}, 'created_by': 'api_user', 'version': '11.0.0', 'create_time': 1701689920521, 'model_size_bytes': 0, 'estimated_operations': 0, 'license_level': 'platinum', 'description': 'Elastic Learned Sparse EncodeR v2', 'tags': ['elastic'], 'metadata': {}, 'input': {'field_names': ['text_field']}, 'inference_config': {'text_expansion': {'vocabulary': {'index': '.ml-inference-native-000002'}, 'tokenization': {'bert': {'do_lower_case': True, 'with_special_tokens': True, 'max_sequence_length': 512, 'truncate': 'first', 'span': -1}}}}, 'location': {'index': {'name': '.ml-inference-native-000002'}}})"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# delete model if already downloaded and deployed\n",
     "try:\n",
@@ -202,7 +228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -237,22 +263,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
-     "ename": "BadRequestError",
-     "evalue": "BadRequestError(400, 'status_exception', 'Could not start model deployment because an existing deployment with the same id [.elser_model_2] exist')",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
-      "\u001b[1;32m/Users/joe/projects/elastic/elasticsearch-labs/notebooks/search/03-ELSER.ipynb Cell 16\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/joe/projects/elastic/elasticsearch-labs/notebooks/search/03-ELSER.ipynb#X21sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Start trained model deployment if not already deployed\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/joe/projects/elastic/elasticsearch-labs/notebooks/search/03-ELSER.ipynb#X21sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m client\u001b[39m.\u001b[39;49mml\u001b[39m.\u001b[39;49mstart_trained_model_deployment(\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/joe/projects/elastic/elasticsearch-labs/notebooks/search/03-ELSER.ipynb#X21sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m   model_id\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m.elser_model_2\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/joe/projects/elastic/elasticsearch-labs/notebooks/search/03-ELSER.ipynb#X21sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m   number_of_allocations\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/joe/projects/elastic/elasticsearch-labs/notebooks/search/03-ELSER.ipynb#X21sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m )\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.3/lib/python3.10/site-packages/elasticsearch/_sync/client/utils.py:402\u001b[0m, in \u001b[0;36m_rewrite_parameters.<locals>.wrapper.<locals>.wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    399\u001b[0m         \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[1;32m    400\u001b[0m             \u001b[39mpass\u001b[39;00m\n\u001b[0;32m--> 402\u001b[0m \u001b[39mreturn\u001b[39;00m api(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.3/lib/python3.10/site-packages/elasticsearch/_sync/client/ml.py:3655\u001b[0m, in \u001b[0;36mMlClient.start_trained_model_deployment\u001b[0;34m(self, model_id, cache_size, error_trace, filter_path, human, number_of_allocations, pretty, priority, queue_capacity, threads_per_allocation, timeout, wait_for)\u001b[0m\n\u001b[1;32m   3653\u001b[0m     __query[\u001b[39m\"\u001b[39m\u001b[39mwait_for\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m wait_for\n\u001b[1;32m   3654\u001b[0m __headers \u001b[39m=\u001b[39m {\u001b[39m\"\u001b[39m\u001b[39maccept\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mapplication/json\u001b[39m\u001b[39m\"\u001b[39m}\n\u001b[0;32m-> 3655\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mperform_request(  \u001b[39m# type: ignore[return-value]\u001b[39;49;00m\n\u001b[1;32m   3656\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39mPOST\u001b[39;49m\u001b[39m\"\u001b[39;49m, __path, params\u001b[39m=\u001b[39;49m__query, headers\u001b[39m=\u001b[39;49m__headers\n\u001b[1;32m   3657\u001b[0m )\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.3/lib/python3.10/site-packages/elasticsearch/_sync/client/_base.py:389\u001b[0m, in \u001b[0;36mNamespacedClient.perform_request\u001b[0;34m(self, method, path, params, headers, body)\u001b[0m\n\u001b[1;32m    378\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mperform_request\u001b[39m(\n\u001b[1;32m    379\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    380\u001b[0m     method: \u001b[39mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    387\u001b[0m     \u001b[39m# Use the internal clients .perform_request() implementation\u001b[39;00m\n\u001b[1;32m    388\u001b[0m     \u001b[39m# so we take advantage of their transport options.\u001b[39;00m\n\u001b[0;32m--> 389\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_client\u001b[39m.\u001b[39;49mperform_request(\n\u001b[1;32m    390\u001b[0m         method, path, params\u001b[39m=\u001b[39;49mparams, headers\u001b[39m=\u001b[39;49mheaders, body\u001b[39m=\u001b[39;49mbody\n\u001b[1;32m    391\u001b[0m     )\n",
-      "File \u001b[0;32m~/.pyenv/versions/3.10.3/lib/python3.10/site-packages/elasticsearch/_sync/client/_base.py:320\u001b[0m, in \u001b[0;36mBaseClient.perform_request\u001b[0;34m(self, method, path, params, headers, body)\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[39mexcept\u001b[39;00m (\u001b[39mValueError\u001b[39;00m, \u001b[39mKeyError\u001b[39;00m, \u001b[39mTypeError\u001b[39;00m):\n\u001b[1;32m    318\u001b[0m             \u001b[39mpass\u001b[39;00m\n\u001b[0;32m--> 320\u001b[0m     \u001b[39mraise\u001b[39;00m HTTP_EXCEPTIONS\u001b[39m.\u001b[39mget(meta\u001b[39m.\u001b[39mstatus, ApiError)(\n\u001b[1;32m    321\u001b[0m         message\u001b[39m=\u001b[39mmessage, meta\u001b[39m=\u001b[39mmeta, body\u001b[39m=\u001b[39mresp_body\n\u001b[1;32m    322\u001b[0m     )\n\u001b[1;32m    324\u001b[0m \u001b[39m# 'X-Elastic-Product: Elasticsearch' should be on every 2XX response.\u001b[39;00m\n\u001b[1;32m    325\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_verified_elasticsearch:\n\u001b[1;32m    326\u001b[0m     \u001b[39m# If the header is set we mark the server as verified.\u001b[39;00m\n",
-      "\u001b[0;31mBadRequestError\u001b[0m: BadRequestError(400, 'status_exception', 'Could not start model deployment because an existing deployment with the same id [.elser_model_2] exist')"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ELSER Model is currently being deployed.\n",
+      "ELSER Model is currently being deployed.\n",
+      "ELSER Model has been successfully deployed.\n"
      ]
     }
    ],
@@ -260,8 +280,22 @@
     "# Start trained model deployment if not already deployed\n",
     "client.ml.start_trained_model_deployment(\n",
     "  model_id=\".elser_model_2\",\n",
-    "  number_of_allocations=1\n",
-    ")\n"
+    "  number_of_allocations=1,\n",
+    "  wait_for=\"starting\"\n",
+    ")\n",
+    "\n",
+    "while True:\n",
+    "  status = client.ml.get_trained_models_stats(\n",
+    "    model_id=\".elser_model_2\",\n",
+    "  )\n",
+    "  if (status[\"trained_model_stats\"][0][\"deployment_stats\"][\"state\"] == \"started\"):\n",
+    "    print(\"ELSER Model has been successfully deployed.\")\n",
+    "    break\n",
+    "  else:\n",
+    "    print(\"ELSER Model is currently being deployed.\")\n",
+    "  time.sleep(5)\n",
+    "\n",
+    "\n"
    ]
   },
   {
@@ -286,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -294,7 +328,18 @@
     "id": "XhRng99KLQsd",
     "outputId": "00ea73b5-45a4-472b-f4bc-2c2c790ab94d"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ObjectApiResponse({'acknowledged': True})"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "client.ingest.put_pipeline(\n",
     "    id=\"elser-ingest-pipeline\", \n",
@@ -349,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 46,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -364,7 +409,7 @@
        "ObjectApiResponse({'acknowledged': True, 'shards_acknowledged': True, 'index': 'elser-example-movies'})"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -375,8 +420,6 @@
     "  index=\"elser-example-movies\",\n",
     "  settings={\n",
     "      \"index\": {\n",
-    "          \"number_of_shards\": 1,\n",
-    "          \"number_of_replicas\": 1,\n",
     "          \"default_pipeline\": \"elser-ingest-pipeline\"\n",
     "      }\n",
     "  },\n",
@@ -414,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 44,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"
@@ -482,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 45,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/"