diff --git a/v3-examples/inference-examples/huggingface-example.ipynb b/v3-examples/inference-examples/huggingface-example.ipynb
index 869b93f759..0a10ec66b2 100644
--- a/v3-examples/inference-examples/huggingface-example.ipynb
+++ b/v3-examples/inference-examples/huggingface-example.ipynb
@@ -9,6 +9,14 @@
     "This notebook demonstrates how to deploy HuggingFace models using SageMaker V3 ModelBuilder for text generation tasks."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prerequisites\n",
+    "Note: Ensure you have sagemaker and ipywidgets installed in your environment. The ipywidgets package is required to monitor endpoint deployment progress in Jupyter notebooks."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -292,7 +300,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-test",
    "language": "python",
    "name": "python3"
   },
@@ -306,9 +314,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/v3-examples/inference-examples/in-process-mode-example.ipynb b/v3-examples/inference-examples/in-process-mode-example.ipynb
index e5ba8c0b79..ecf5c0c7e5 100644
--- a/v3-examples/inference-examples/in-process-mode-example.ipynb
+++ b/v3-examples/inference-examples/in-process-mode-example.ipynb
@@ -313,7 +313,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-test",
    "language": "python",
    "name": "python3"
   },
@@ -327,9 +327,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/v3-examples/inference-examples/inference-spec-example.ipynb b/v3-examples/inference-examples/inference-spec-example.ipynb
index d22cceaec1..9f2f583376 100644
--- a/v3-examples/inference-examples/inference-spec-example.ipynb
+++ b/v3-examples/inference-examples/inference-spec-example.ipynb
@@ -9,6 +9,14 @@
     "This notebook demonstrates how to create and deploy custom models using InferenceSpec with SageMaker V3 ModelBuilder."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prerequisites\n",
+    "Note: Ensure you have sagemaker and ipywidgets installed in your environment. The ipywidgets package is required to monitor endpoint deployment progress in Jupyter notebooks.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -311,7 +319,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-test",
    "language": "python",
    "name": "python3"
   },
@@ -325,9 +333,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/v3-examples/inference-examples/jumpstart-e2e-training-example.ipynb b/v3-examples/inference-examples/jumpstart-e2e-training-example.ipynb
index 6877772e5f..dc5af25436 100644
--- a/v3-examples/inference-examples/jumpstart-e2e-training-example.ipynb
+++ b/v3-examples/inference-examples/jumpstart-e2e-training-example.ipynb
@@ -9,6 +9,14 @@
     "This notebook demonstrates how to use SageMaker V3 to train a JumpStart model from scratch and deploy it for inference."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prerequisites\n",
+    "Note: Ensure you have sagemaker and ipywidgets installed in your environment. The ipywidgets package is required to monitor endpoint deployment progress in Jupyter notebooks.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -41,7 +49,7 @@
    "outputs": [],
    "source": [
     "# Configuration\n",
-    "MODEL_ID = \"huggingface-llm-falcon-7b-bf16\"\n",
+    "MODEL_ID = \"huggingface-spc-bert-base-cased\"\n",
     "MODEL_NAME_PREFIX = \"js-e2e-example-model\"\n",
     "ENDPOINT_NAME_PREFIX = \"js-e2e-example-endpoint\"\n",
     "\n",
@@ -62,7 +70,7 @@
    "source": [
     "## Step 2: Train the Model\n",
     "\n",
-    "Use ModelTrainer to train the JumpStart model. This step will take significant time."
+    "Use ModelTrainer to train the JumpStart model. The training job may take 30+ minutes to complete."
    ]
   },
   {
@@ -75,11 +83,15 @@
     "jumpstart_config = JumpStartConfig(model_id=MODEL_ID)\n",
     "\n",
     "# Initialize ModelTrainer from JumpStart config\n",
-    "model_trainer = ModelTrainer.from_jumpstart_config(jumpstart_config=jumpstart_config)\n",
+    "model_trainer = ModelTrainer.from_jumpstart_config(\n",
+    "    jumpstart_config=jumpstart_config, \n",
+    "    base_job_name=training_job_name, \n",
+    "    hyperparameters={\"epochs\": 1}\n",
+    ")\n",
     "\n",
     "# Train the model\n",
     "print(\"Starting model training...\")\n",
-    "model_trainer.train(training_job_name=training_job_name)\n",
+    "model_trainer.train()\n",
     "print(f\"Training completed: {training_job_name}\")"
    ]
   },
@@ -99,9 +111,10 @@
    "outputs": [],
    "source": [
     "# Build ModelBuilder from trained model\n",
-    "print(\"Building model from training artifacts...\")\n",
-    "model_builder = model_trainer.build()\n",
-    "print(\"ModelBuilder created successfully from training artifacts!\")"
+    "model_builder = ModelBuilder(\n",
+    "    model=model_trainer,\n",
+    "    dependencies={\"auto\": False}\n",
+    ")"
    ]
   },
   {
@@ -150,7 +163,9 @@
    "source": [
     "## Step 6: Test the Endpoint\n",
     "\n",
-    "Send a test request to the deployed endpoint."
+    "This endpoint performs text entailment classification, determining the logical relationship between pairs of sentences. The returned scores indicate the model's confidence for different entailment categories (e.g., entailment, contradiction, neutral) - higher scores indicate stronger predictions for each relationship type. \n",
+    "\n",
+    "This sends a test request to the deployed endpoint."
    ]
   },
   {
@@ -160,16 +175,15 @@
    "outputs": [],
    "source": [
     "# Test with a sample query for the trained model\n",
-    "test_data = {\"inputs\": \"What are falcons?\", \"parameters\": {\"max_new_tokens\": 32}}\n",
+    "test_data = [\"The weather is sunny today\", \"It is not raining\"]\n",
     "\n",
     "result = core_endpoint.invoke(\n",
     "    body=json.dumps(test_data),\n",
-    "    content_type=\"application/json\"\n",
+    "    content_type=\"application/list-text\"\n",
     ")\n",
     "\n",
-    "# Decode the output of the invocation and print the result\n",
-    "prediction = json.loads(result.body.read().decode('utf-8'))\n",
-    "print(f\"Result of invoking trained endpoint: {prediction}\")"
+    "entailment_scores = json.loads(result.body.read().decode('utf-8'))\n",
+    "print(f\"Result of invoking trained endpoint: {entailment_scores}\")\n"
    ]
   },
   {
@@ -218,7 +232,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-test",
    "language": "python",
    "name": "python3"
   },
@@ -232,7 +246,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/v3-examples/inference-examples/jumpstart-example.ipynb b/v3-examples/inference-examples/jumpstart-example.ipynb
index a5cfbbfd40..22f1d0df79 100644
--- a/v3-examples/inference-examples/jumpstart-example.ipynb
+++ b/v3-examples/inference-examples/jumpstart-example.ipynb
@@ -9,6 +9,14 @@
     "This notebook demonstrates how to use SageMaker V3 ModelBuilder with JumpStart models for easy model deployment and inference."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prerequisites\n",
+    "Note: Ensure you have sagemaker and ipywidgets installed in your environment. The ipywidgets package is required to monitor endpoint deployment progress in Jupyter notebooks."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -21,7 +29,8 @@
     "\n",
     "from sagemaker.serve.model_builder import ModelBuilder\n",
     "from sagemaker.core.jumpstart.configs import JumpStartConfig\n",
-    "from sagemaker.core.resources import EndpointConfig"
+    "from sagemaker.core.resources import EndpointConfig\n",
+    "from sagemaker.train.configs import Compute"
    ]
   },
   {
@@ -69,8 +78,9 @@
    "outputs": [],
    "source": [
     "# Initialize model_builder object with JumpStart configuration\n",
+    "compute = Compute(instance_type=\"ml.g5.2xlarge\")\n",
     "jumpstart_config = JumpStartConfig(model_id=MODEL_ID)\n",
-    "model_builder = ModelBuilder.from_jumpstart_config(jumpstart_config=jumpstart_config)\n",
+    "model_builder = ModelBuilder.from_jumpstart_config(jumpstart_config=jumpstart_config, compute=compute)\n",
     "\n",
     "print(\"ModelBuilder created successfully from JumpStart config!\")"
    ]
@@ -188,7 +198,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-test",
    "language": "python",
    "name": "python3"
   },
@@ -202,7 +212,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/v3-examples/inference-examples/local-mode-example.ipynb b/v3-examples/inference-examples/local-mode-example.ipynb
index d848ebad67..45f4de02e6 100644
--- a/v3-examples/inference-examples/local-mode-example.ipynb
+++ b/v3-examples/inference-examples/local-mode-example.ipynb
@@ -31,6 +31,19 @@
     "from sagemaker.serve.mode.function_pointers import Mode"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NOTE: Local mode requires Docker to be installed and running. \n",
+    "# If Docker is not in your system PATH, you may need to define the Docker path in one of the top cells.\n",
+    "# Here is an example:\n",
+    "import os\n",
+    "os.environ['PATH'] = '/usr/local/bin:/Applications/Docker.app/Contents/Resources/bin:' + os.environ['PATH']"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -217,7 +230,7 @@
    "source": [
     "## Step 6: Deploy in Local Container\n",
     "\n",
-    "Deploy the model in a local Docker container. This may take a few minutes to pull the container image."
+    "Deploy the model in a local Docker container. This may take a few minutes to pull the container image and ping the container until it is live. This is a normal part of the deployment process."
    ]
   },
   {
@@ -395,7 +408,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-test",
    "language": "python",
    "name": "python3"
   },
@@ -409,9 +422,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/v3-examples/inference-examples/optimize-example.ipynb b/v3-examples/inference-examples/optimize-example.ipynb
index 2ef1b0af1d..4ad4f3b513 100644
--- a/v3-examples/inference-examples/optimize-example.ipynb
+++ b/v3-examples/inference-examples/optimize-example.ipynb
@@ -9,6 +9,14 @@
     "This notebook demonstrates how to use SageMaker V3 ModelBuilder to optimize a JumpStart model for improved inference performance."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prerequisites\n",
+    "Note: Ensure you have sagemaker and ipywidgets installed in your environment. The ipywidgets package is required to monitor endpoint deployment progress in Jupyter notebooks."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -124,7 +132,6 @@
     "    model=MODEL_ID,\n",
     "    schema_builder=schema_builder,\n",
     "    sagemaker_session=sagemaker_session,\n",
-    "    model_name=model_name,\n",
     ")\n",
     "print(\"ModelBuilder created successfully!\")"
    ]
@@ -135,7 +142,7 @@
    "source": [
     "## Step 5: Optimize the Model\n",
     "\n",
-    "Optimize the model using AWQ quantization for improved inference performance."
+    "Optimize the model using AWQ quantization for improved inference performance. This step may take up to 30 minutes to complete!"
    ]
   },
   {
@@ -152,7 +159,8 @@
     "    output_path=f\"s3://sagemaker-us-east-2-593793038179/optimize-output/jumpstart-{unique_id}/\",\n",
     "    quantization_config={\"OverrideEnvironment\": {\"OPTION_QUANTIZE\": \"awq\"}},\n",
     "    accept_eula=True,\n",
-    "    job_name=job_name\n",
+    "    job_name=job_name,\n",
+    "    model_name=model_name,\n",
     ")\n",
     "print(f\"Model Successfully Optimized: {optimized_model.model_name}\")"
    ]
@@ -257,7 +265,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-test",
    "language": "python",
    "name": "python3"
   },
@@ -271,7 +279,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/v3-examples/inference-examples/train-inference-e2e-example.ipynb b/v3-examples/inference-examples/train-inference-e2e-example.ipynb
index 9959928df1..b0f62e8293 100644
--- a/v3-examples/inference-examples/train-inference-e2e-example.ipynb
+++ b/v3-examples/inference-examples/train-inference-e2e-example.ipynb
@@ -9,6 +9,14 @@
     "This notebook demonstrates the complete end-to-end workflow from training a custom PyTorch model to deploying it for inference using SageMaker V3."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prerequisites\n",
+    "Note: Ensure you have sagemaker and ipywidgets installed in your environment. The ipywidgets package is required to monitor endpoint deployment progress in Jupyter notebooks."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -382,7 +390,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-test",
    "language": "python",
    "name": "python3"
   },
@@ -396,9 +404,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}