Update titles for BYO and XGBoost multi model notebooks

Author: Michael Pham

advanced_functionality/multi_model_bring_your_own/multi_model_endpoint_bring_your_own.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Amazon SageMaker Multi-Model Endpoints using Scikit Learn\n",
+    "# Amazon SageMaker Multi-Model Endpoints using your own algorithm container\n",
     "With [Amazon SageMaker multi-model endpoints](https://docs.aws.amazon.com/sagemaker/latest/dg/multi-model-endpoints.html), customers can create an endpoint that seamlessly hosts up to thousands of models. These endpoints are well suited to use cases where any one of a large number of models, which can be served from a common inference container, needs to be invokable on-demand and where it is acceptable for infrequently invoked models to incur some additional latency. For applications which require consistently low inference latency, a traditional endpoint is still the best choice.\n",
     "\n",
     "At a high level, Amazon SageMaker manages the loading and unloading of models for a multi-model endpoint, as they are needed. When an invocation request is made for a particular model, Amazon SageMaker routes the request to an instance assigned to that model, downloads the model artifacts from S3 onto that instance, and initiates loading of the model into the memory of the container. As soon as the loading is complete, Amazon SageMaker performs the requested invocation and returns the result. If the model is already loaded in memory on the selected instance, the downloading and loading steps are skipped and the invocation is performed immediately.\n",

advanced_functionality/multi_model_xgboost_home_value/xgboost_multi_model_endpoint_home_value.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Amazon SageMaker Multi-Model Endpoints using Scikit Learn\n",
+    "# Amazon SageMaker Multi-Model Endpoints using XGBoost\n",
     "With [Amazon SageMaker multi-model endpoints](https://docs.aws.amazon.com/sagemaker/latest/dg/multi-model-endpoints.html), customers can create an endpoint that seamlessly hosts up to thousands of models. These endpoints are well suited to use cases where any one of a large number of models, which can be served from a common inference container, needs to be invokable on-demand and where it is acceptable for infrequently invoked models to incur some additional latency. For applications which require consistently low inference latency, a traditional endpoint is still the best choice.\n",
     "\n",
     "At a high level, Amazon SageMaker manages the loading and unloading of models for a multi-model endpoint, as they are needed. When an invocation request is made for a particular model, Amazon SageMaker routes the request to an instance assigned to that model, downloads the model artifacts from S3 onto that instance, and initiates loading of the model into the memory of the container. As soon as the loading is complete, Amazon SageMaker performs the requested invocation and returns the result. If the model is already loaded in memory on the selected instance, the downloading and loading steps are skipped and the invocation is performed immediately.\n",