Update sm_xgboost_ca_housing_apprunner_model_hosting.ipynb

Author: gv-tech

notebooks/sm_xgboost_ca_housing_apprunner_model_hosting.ipynb

@@ -5,7 +5,7 @@
    "id": "6964c5e9",
    "metadata": {},
    "source": [
-    "# Train a XGBoost regression model on Amazon SageMaker, host inference as an API on a Docker container running on AWS App Runner\n",
+    "# Train a XGBoost regression model on Amazon SageMaker and host inference as an API on a Docker container running on AWS App Runner\n",
     "\n",
     "[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed end-to-end Machine Learning (ML) service. With SageMaker, you have the option of using the built-in algorithms or you can bring your own algorithms and frameworks to train your models.  After training, you can deploy the models in [one of two ways](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-model.html) for inference - persistent endpoint or batch transform.\n",
     "\n",