diff --git a/guides/computer_vision/Computer_Vision_with_Comet.ipynb b/guides/computer_vision/Computer_Vision_with_Comet.ipynb
index 2322d59e..4986f9bc 100644
--- a/guides/computer_vision/Computer_Vision_with_Comet.ipynb
+++ b/guides/computer_vision/Computer_Vision_with_Comet.ipynb
@@ -61,7 +61,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install comet_ml torch torchvision timm datasets h5py scipy scikit-learn \"pandas<2\""
+    "%pip install comet_ml torch torchvision timm datasets h5py scipy scikit-learn \"pandas<2\" \"numpy<2\""
    ]
   },
   {
@@ -131,12 +131,16 @@
     "\n",
     "# Sample 1000 examples from the training split as our training set\n",
     "NUM_TRAIN_SAMPLES = 1000\n",
-    "train_dataset = load_dataset(\"svhn\", \"cropped_digits\", split=\"train\", streaming=True)\n",
+    "train_dataset = load_dataset(\n",
+    "    \"svhn\", \"cropped_digits\", split=\"train\", streaming=True, trust_remote_code=True\n",
+    ")\n",
     "train_dataset = train_dataset.take(NUM_TRAIN_SAMPLES)\n",
     "\n",
     "# Sample 100 examples from the training split as our test set\n",
     "NUM_TEST_SAMPLES = 100\n",
-    "test_dataset = load_dataset(\"svhn\", \"cropped_digits\", split=\"test\", streaming=True)\n",
+    "test_dataset = load_dataset(\n",
+    "    \"svhn\", \"cropped_digits\", split=\"test\", streaming=True, trust_remote_code=True\n",
+    ")\n",
     "test_dataset = test_dataset.take(NUM_TEST_SAMPLES)\n",
     "\n",
     "transforms = T.Compose([T.ToTensor(), T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
@@ -640,9 +644,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.15"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/guides/get-started/Comet_Quickstart.ipynb b/guides/get-started/Comet_Quickstart.ipynb
index 3b740a92..4daf4e3c 100644
--- a/guides/get-started/Comet_Quickstart.ipynb
+++ b/guides/get-started/Comet_Quickstart.ipynb
@@ -76,7 +76,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install -U comet_ml"
+    "%pip install -U comet_ml \"numpy<2.0.0\""
    ]
   },
   {
@@ -705,9 +705,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/integrations/model-training/prophet/notebooks/Comet_and_Prophet.ipynb b/integrations/model-training/prophet/notebooks/Comet_and_Prophet.ipynb
index e1274309..ae82b133 100644
--- a/integrations/model-training/prophet/notebooks/Comet_and_Prophet.ipynb
+++ b/integrations/model-training/prophet/notebooks/Comet_and_Prophet.ipynb
@@ -45,7 +45,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install comet_ml prophet plotly"
+    "%pip install comet_ml prophet plotly \"numpy<2.0.0\""
    ]
   },
   {
@@ -212,9 +212,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/integrations/model-training/pytorch-lightning/notebooks/Comet_and_Pytorch_Lightning.ipynb b/integrations/model-training/pytorch-lightning/notebooks/Comet_and_Pytorch_Lightning.ipynb
index b8a42c82..557c0197 100644
--- a/integrations/model-training/pytorch-lightning/notebooks/Comet_and_Pytorch_Lightning.ipynb
+++ b/integrations/model-training/pytorch-lightning/notebooks/Comet_and_Pytorch_Lightning.ipynb
@@ -61,7 +61,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install torch torchvision \"pytorch-lightning<2.0.0\""
+    "%pip install torch torchvision \"pytorch-lightning<2.0.0\" \"numpy<2.0.0\""
    ]
   },
   {
@@ -249,9 +249,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }
diff --git a/integrations/reinforcement-learning/gymnasium/notebooks/comet_gymnasium_example.ipynb b/integrations/reinforcement-learning/gymnasium/notebooks/comet_gymnasium_example.ipynb
index 1a61b1d7..130bf3eb 100644
--- a/integrations/reinforcement-learning/gymnasium/notebooks/comet_gymnasium_example.ipynb
+++ b/integrations/reinforcement-learning/gymnasium/notebooks/comet_gymnasium_example.ipynb
@@ -33,7 +33,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install 'gymnasium[classic-control]' comet_ml stable-baselines3"
+    "%pip install 'gymnasium[classic-control]' comet_ml stable-baselines3 \"numpy<2.0.0\""
    ]
   },
   {
diff --git a/integrations/workflow-orchestration/metaflow/metaflow-model-evaluation/metaflow-model-evaluation.py b/integrations/workflow-orchestration/metaflow/metaflow-model-evaluation/metaflow-model-evaluation.py
index 4ef9e9c2..457665a3 100644
--- a/integrations/workflow-orchestration/metaflow/metaflow-model-evaluation/metaflow-model-evaluation.py
+++ b/integrations/workflow-orchestration/metaflow/metaflow-model-evaluation/metaflow-model-evaluation.py
@@ -178,7 +178,10 @@ def evaluate_classification_metrics(self):
 
         device = "cuda" if torch.cuda.is_available() else "cpu"
         dataset = load_dataset(
-            self.dataset_name, split=self.dataset_split, streaming=True
+            self.dataset_name,
+            split=self.dataset_split,
+            streaming=True,
+            trust_remote_code=True,
         )
         dataset = dataset.shuffle(self.seed, buffer_size=10_000)
         dataset = dataset.take(self.n_samples)