test ci notebooks

advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb

@@ -6,6 +6,8 @@
    "source": [
     "# Building your own algorithm container\n",
     "\n",
+    "test ci.\n",
+    "\n",
     "With Amazon SageMaker, you can package your own algorithms that can than be trained and deployed in the SageMaker environment. This notebook will guide you through an example that shows you how to build a Docker container for SageMaker and use it for training and inference.\n",
     "\n",
     "By packaging an algorithm in a container, you can bring almost any code to the Amazon SageMaker environment, regardless of programming language, environment, framework, or dependencies. \n",

sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing.ipynb

@@ -6,6 +6,8 @@
    "source": [
     "# Get started with SageMaker Processing\n",
     "\n",
+    "test ci.\n",
+    "\n",
     "This notebook corresponds to the section \"Preprocessing Data With The Built-In Scikit-Learn Container\" in the blog post [Amazon SageMaker Processing – Fully Managed Data Processing and Model Evaluation](https://aws.amazon.com/blogs/aws/amazon-sagemaker-processing-fully-managed-data-processing-and-model-evaluation/). \n",
     "It shows a lightweight example of using SageMaker Processing to create train, test, and validation datasets. SageMaker Processing is used to create these datasets, which then are written back to S3.\n",
     "\n",