diff --git a/conditional-generation/conditional-generation.ipynb b/conditional-generation/conditional-generation.ipynb
index f1e5e34..aced5ec 100644
--- a/conditional-generation/conditional-generation.ipynb
+++ b/conditional-generation/conditional-generation.ipynb
@@ -246,7 +246,7 @@
     "    'tables': [{\n",
     "        'name': 'AirBnB',\n",
     "        'data': df,\n",
-    "        'model_configuration': {'max_training_time': 2},\n",
+    "        'tabular_model_configuration': {'max_training_time': 2},\n",
     "        'columns': [\n",
     "            {'name': 'neighbourhood_group', 'included': True, 'model_encoding_type': 'TABULAR_CATEGORICAL'},\n",
     "            {'name': 'neighbourhood', 'included': True, 'model_encoding_type': 'TABULAR_CATEGORICAL'},\n",
diff --git a/smart-imputation/smart-imputation.ipynb b/smart-imputation/smart-imputation.ipynb
index 7643eac..4ec5671 100644
--- a/smart-imputation/smart-imputation.ipynb
+++ b/smart-imputation/smart-imputation.ipynb
@@ -115,7 +115,14 @@
     "mostly = MostlyAI(local=True)  # or: MostlyAI(local=False, api_key='xxx')\n",
     "\n",
     "# train a generator on the original training data\n",
-    "g = mostly.train(data=tgt, name='Smart Imputation Tutorial - Census')"
+    "g = mostly.train(config={\n",
+    "    'name': 'Smart Imputation Tutorial - Census',\n",
+    "    'tables': [{\n",
+    "        'name': 'data',\n",
+    "        'data': tgt,\n",
+    "        'tabular_model_configuration': {'max_training_time': 1}\n",
+    "    }]\n",
+    "})"
    ]
   },
   {
@@ -131,7 +138,7 @@
     "    'tables': [\n",
     "        {'name': 'data',\n",
     "            'configuration': {\n",
-    "                'imputation': ['age']\n",
+    "                'imputation': {'columns': ['age']},\n",
     "            }\n",
     "        }\n",
     "    ]\n",
@@ -148,7 +155,7 @@
    "id": "97058dc7-b791-4674-b028-9599384b9d7f",
    "metadata": {},
    "source": [
-    "If you want to, you can now switch to the UI and check the distribution based on the Model QA and Data QA reports. The Model QA reports on the accuracy and privacy of the trained Generative AI model. As one can see, the distributiosn are faithfully learned, and also include the right share of missing values. The Data QA visualizes then the distributions of the delivered Synthetic dataset. And there we can see, that the share of missing values (`N/A`) has dropped to 0%, and that the distribution has been shifted towards older age buckets."
+    "If you want to, you can now check the distribution based on the Model QA and Data QA reports (`sd.reports()`). The Model QA reports on the accuracy and privacy of the trained Generative AI model. As one can see, the distributiosn are faithfully learned, and also include the right share of missing values. The Data QA visualizes then the distributions of the delivered Synthetic dataset. And there we can see, that the share of missing values (`N/A`) has dropped to 0%, and that the distribution has been shifted towards older age buckets."
    ]
   },
   {
diff --git a/synthetic-text/synthetic-text.ipynb b/synthetic-text/synthetic-text.ipynb
index f4cdb20..e75155f 100644
--- a/synthetic-text/synthetic-text.ipynb
+++ b/synthetic-text/synthetic-text.ipynb
@@ -59,7 +59,7 @@
     "    'name': 'Synthetic Text Tutorial AirBnB',\n",
     "    'tables': [{\n",
     "        'name': 'airBnB', 'data': tgt,\n",
-    "        'model_configuration': {'max_training_time': 10},\n",
+    "        'tabular_model_configuration': {'max_training_time': 10},\n",
     "        'language_model_configuration': {'max_training_time': 10},\n",
     "        'columns': [\n",
     "            {'name': 'host_name', 'included': True, 'model_encoding_type': 'LANGUAGE_TEXT'},\n",