Change TFS refenrence.

Author: Chuyang Deng

sagemaker-python-sdk/tensorflow_serving_using_elastic_inference_with_your_own_model/tensorflow_serving_pretrained_model_elastic_inference.ipynb

@@ -38,7 +38,7 @@
     "\n",
     "The pre-trained model we will be using for this example is a NCHW ResNet-50 model from the [official Tensorflow model Github repository](https://github.com/tensorflow/models/tree/master/official/resnet#pre-trained-model). For more information in regards to deep residual networks, please check [here](https://github.com/tensorflow/models/tree/master/official/resnet). It isn't a requirement to train our model on SageMaker to use SageMaker for serving our model.\n",
     "\n",
-    "SageMaker expects our models to be compressed in a tar.gz format in S3. Thankfully, our model already comes in that format. The predefined TensorFlow Serving containers use REST API for handling inferences, for more informationm, please see [predefined SageMaker TensorFlow Serving](https://github.com/aws/sagemaker-tensorflow-serving-container/blob/master/container/sagemaker/serve.py).\n",
+    "SageMaker expects our models to be compressed in a tar.gz format in S3. Thankfully, our model already comes in that format. The predefined TensorFlow Serving containers use REST API for handling inferences, for more informationm, please see [Deploying to TensorFlow Serving Endpoints](https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/tensorflow/deploying_tensorflow_serving.rst#making-predictions-against-a-sagemaker-endpoint).\n",
     "\n",
     "To host our model for inferences in SageMaker, we need to first upload the SavedModel to S3. This can be done through the AWS console or AWS command line.\n",
     "\n",