Fix H1 headings (#3300)

Author: jkroll-aws

sagemaker-experiments/mnist-handwritten-digits-classification-experiment/mnist-handwritten-digits-classification-experiment.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## MNIST Handwritten Digits Classification Experiment\n",
+    "# MNIST Handwritten Digits Classification Experiment\n",
     "\n",
     "This demo shows how you can use SageMaker Experiment Management Python SDK to organize, track, compare, and evaluate your machine learning (ML) model training experiments.\n",
     "\n",

sagemaker-python-sdk/mxnet_gluon_mnist/mxnet_mnist_with_gluon.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## MNIST Training with MXNet and Gluon\n",
+    "# MNIST Training with MXNet and Gluon\n",
     "\n",
     "MNIST is a widely used dataset for handwritten digit classification. It consists of 70,000 labeled 28x28 pixel grayscale images of hand-written digits. The dataset is split into 60,000 training images and 10,000 test images. There are 10 classes (one for each of the 10 digits). This tutorial will show how to train and test an MNIST model on SageMaker using MXNet and the Gluon API.\n",
     "\n"

sagemaker-script-mode/pytorch_bert/deploy_bert.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Hosting a Pretrained Model on SageMaker\n",
+    "# Hosting a Pretrained Model on SageMaker\n",
     "    \n",
     "Amazon SageMaker is a service to accelerate the entire machine learning lifecycle. It includes components for building, training and deploying machine learning models. Each SageMaker component is modular, so you're welcome to only use the features needed for your use case. One of the most popular features of SageMaker is [model hosting](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-deployment.html). Using SageMaker Hosting you can deploy your model as a scalable, highly available, multi-process API endpoint with a few lines of code. In this notebook, we will demonstrate how to host a pretrained model (BERT) in Amazon SageMaker to extract embeddings from text.\n",
     "\n",

sagemaker_batch_transform/pytorch_mnist_batch_transform/pytorch-mnist-batch-transform.ipynb

@@ -217,7 +217,7 @@
     "tags": []
    },
    "source": [
-    "# Prepare batch inference data\n",
+    "## Prepare batch inference data\n",
     "\n",
     "Convert the test data into PNG image format."
    ]
@@ -337,7 +337,7 @@
     "tags": []
    },
    "source": [
-    "# Create model transformer\n",
+    "## Create model transformer\n",
     "Now, we create a transformer object for creating and interacting with Amazon SageMaker transform jobs. We can create the transformer in two ways:\n",
     "1. Use a fitted estimator directly.\n",
     "1. First create a PyTorchModel from a saved model artifact, and then create a transformer from the PyTorchModel object.\n",

sagemaker_model_monitor/introduction/SageMaker-ModelMonitoring.ipynb

@@ -96,7 +96,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# PART A: Capturing real-time inference data from Amazon SageMaker endpoints\n",
+    "## PART A: Capturing real-time inference data from Amazon SageMaker endpoints\n",
     "Create an endpoint to showcase the data capture capability in action.\n",
     "\n",
     "### Upload the pre-trained model to Amazon S3\n",
@@ -294,7 +294,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# PART B: Model Monitor - Baselining and continuous monitoring"
+    "## PART B: Model Monitor - Baselining and continuous monitoring"
    ]
   },
   {