docs: Add Bedrock model builder example notebooks

Add notebooks demonstrating Bedrock deployment workflows:
- bedrock-modelbuilder-deployment-nova.ipynb: Nova model deployment
  via BedrockModelBuilder with SFTTrainer fine-tuning
- boto3_deployment_notebook.ipynb: Direct boto3 Bedrock deployment
- model_builder_deployment_notebook(1).ipynb: ModelBuilder deployment
- 07-ml-model-development(1).ipynb: ML model development workflow
- sagemaker-serve/example_notebooks/bedrock_nova_deployment.ipynb:
  Clean Nova deployment example with polling, inference, and cleanup

Author: nargokul

sagemaker-serve/example_notebooks/bedrock_nova_deployment.ipynb

@@ -22,7 +22,7 @@
     "4. Clean up resources\n",
     "\n",
     "**Prerequisites:**\n",
-    "- AWS credentials with SageMaker and Bedrock access in `us-east-1`\n",
+    "- AWS credentials with SageMaker and Bedrock access\n",
     "- `sagemaker-serve` and `sagemaker-train` packages installed\n",
     "- An IAM role with Bedrock and SageMaker permissions"
    ]
@@ -46,8 +46,10 @@
     "import random\n",
     "import boto3\n",
     "\n",
+    "# Set your region — Nova fine-tuning is available in us-east-1\n",
     "REGION = \"us-east-1\"\n",
     "os.environ[\"AWS_DEFAULT_REGION\"] = REGION\n",
+    "os.environ[\"SAGEMAKER_REGION\"] = REGION\n",
     "\n",
     "from sagemaker.core.helper.session_helper import get_execution_role\n",
     "\n",
@@ -78,6 +80,16 @@
    "source": [
     "s3 = boto3.client(\"s3\", region_name=REGION)\n",
     "\n",
+    "# Ensure the bucket exists\n",
+    "try:\n",
+    "    s3.head_bucket(Bucket=bucket)\n",
+    "except Exception:\n",
+    "    s3.create_bucket(\n",
+    "        Bucket=bucket,\n",
+    "        CreateBucketConfiguration={\"LocationConstraint\": REGION},\n",
+    "    )\n",
+    "    print(f\"Created bucket: {bucket}\")\n",
+    "\n",
     "train_key = \"nova-example/train.jsonl\"\n",
     "train_uri = f\"s3://{bucket}/{train_key}\"\n",
     "\n",
@@ -98,7 +110,38 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 2: Fine-tune Nova Micro with SFTTrainer\n",
+    "## Step 2: Create model package group\n",
+    "\n",
+    "SFTTrainer requires a model package group to register the fine-tuned model.\n",
+    "We create one if it doesn't already exist."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sm = boto3.client(\"sagemaker\", region_name=REGION)\n",
+    "\n",
+    "MODEL_PACKAGE_GROUP = f\"nova-example-{account_id}\"\n",
+    "\n",
+    "try:\n",
+    "    sm.describe_model_package_group(ModelPackageGroupName=MODEL_PACKAGE_GROUP)\n",
+    "    print(f\"Model package group already exists: {MODEL_PACKAGE_GROUP}\")\n",
+    "except sm.exceptions.ClientError:\n",
+    "    sm.create_model_package_group(\n",
+    "        ModelPackageGroupName=MODEL_PACKAGE_GROUP,\n",
+    "        ModelPackageGroupDescription=\"Nova fine-tuning example models\",\n",
+    "    )\n",
+    "    print(f\"Created model package group: {MODEL_PACKAGE_GROUP}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3: Fine-tune Nova Micro with SFTTrainer\n",
     "\n",
     "This launches a SageMaker training job. It typically takes 15-30 minutes to complete."
    ]
@@ -115,7 +158,7 @@
     "    model=\"nova-textgeneration-micro\",\n",
     "    training_dataset=train_uri,\n",
     "    accept_eula=True,\n",
-    "    model_package_group=\"nova-example-models\",\n",
+    "    model_package_group=MODEL_PACKAGE_GROUP,\n",
     ")\n",
     "\n",
     "# Set wait=True to block until training completes\n",
@@ -130,7 +173,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 3: Deploy to Bedrock with BedrockModelBuilder\n",
+    "## Step 4: Deploy to Bedrock with BedrockModelBuilder\n",
     "\n",
     "The builder handles the full deployment flow:\n",
     "- Fetches the model package from the training job\n",
@@ -165,7 +208,7 @@
     "deployment_name = f\"{custom_model_name}-dep\"\n",
     "\n",
     "print(f\"Deploying as: {custom_model_name}\")\n",
-    "print(f\"This will poll for model creation and deployment — may take several minutes...\")\n",
+    "print(\"This will poll for model creation and deployment — may take several minutes...\")\n",
     "\n",
     "response = builder.deploy(\n",
     "    custom_model_name=custom_model_name,\n",
@@ -182,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 4: Test inference (optional)\n",
+    "## Step 5: Test inference (optional)\n",
     "\n",
     "Once the deployment is Active, you can invoke it via the Bedrock Runtime API."
    ]
@@ -220,7 +263,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Step 5: Cleanup\n",
+    "## Step 6: Cleanup\n",
     "\n",
     "Delete the deployment and custom model to avoid ongoing charges."
    ]