fixed a couple references to 'IM' with SM

advanced_functionality/scikit_bring_your_own/container/ReadMe.md

@@ -4,7 +4,7 @@ This example shows how to package an algorithm for use with SageMaker. We have c
 
 SageMaker supports two execution modes: _training_ where the algorithm uses input data to train a new model and _serving_ where the algorithm accepts HTTP requests and uses the previously trained model to do an inference (also called "scoring", "prediction", or "transformation").
 
-The algorithm that we have built here supports both training and scoring in IM with the same container image. It is perfectly reasonable to build an algorithm that supports only training _or_ scoring as well as to build an algorithm that has separate container images for training and scoring.v
+The algorithm that we have built here supports both training and scoring in SageMaker with the same container image. It is perfectly reasonable to build an algorithm that supports only training _or_ scoring as well as to build an algorithm that has separate container images for training and scoring.v
 
 In order to build a production grade inference server into the container, we use the following stack to make the implementer's job simple:
 
@@ -18,7 +18,7 @@ The components are as follows:
 
 * __Dockerfile__: The _Dockerfile_ describes how the image is built and what it contains. It is a recipe for your container and gives you tremendous flexibility to construct almost any execution environment you can imagine. Here. we use the Dockerfile to describe a pretty standard python science stack and the simple scripts that we're going to add to it. See the [Dockerfile reference][dockerfile] for what's possible here.
 
-* __build\_and\_push.sh__: The script to build the Docker image (using the Dockerfile above) and push it to the [Amazon EC2 Container Registry (ECR)][ecr] so that it can be deployed to IM. Specify the name of the image as the argument to this script. The script will generate a full name for the repository in your account and your configured AWS region. If this ECR repository doesn't exist, the script will create it.
+* __build\_and\_push.sh__: The script to build the Docker image (using the Dockerfile above) and push it to the [Amazon EC2 Container Registry (ECR)][ecr] so that it can be deployed to SageMaker. Specify the name of the image as the argument to this script. The script will generate a full name for the repository in your account and your configured AWS region. If this ECR repository doesn't exist, the script will create it.
 
 * __im-decision-trees__: The directory that contains the application to run in the container. See the next session for details about each of the files.
 
@@ -46,7 +46,7 @@ The subdirectory local-test contains scripts and sample data for testing the bui
 
 #### The directory tree mounted into the container
 
-The tree under test-dir is mounted into the container and mimics the directory structure that IM would create for the running container during training or hosting.
+The tree under test-dir is mounted into the container and mimics the directory structure that SageMaker would create for the running container during training or hosting.
 
 * __input/config/hyperparameters.json__: The hyperparameters for the training job.
 * __input/data/training/leaf_train.csv__: The training data.