Update ReadMe.md

Author: djarpin

advanced_functionality/scikit_bring_your_own/container/ReadMe.md

@@ -20,13 +20,13 @@ The components are as follows:
 
 * __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.
+* __decision-trees__: The directory that contains the application to run in the container. See the next session for details about each of the files.
 
 * __local-test__: A directory containing scripts and a setup for running a simple training and inference jobs locally so that you can test that everything is set up correctly. See below for details.
 
 ### The application run inside the container
 
-When IM starts a container, it will invoke the container with an argument of either __train__ or __serve__. We have set this container up so that the argument in treated as the command that the container executes. When training, it will run the __train__ program included and, when serving, it will run the __serve__ program.
+When SageMaker starts a container, it will invoke the container with an argument of either __train__ or __serve__. We have set this container up so that the argument in treated as the command that the container executes. When training, it will run the __train__ program included and, when serving, it will run the __serve__ program.
 
 * __train__: The main program for training the model. When you build your own algorithm, you'll edit this to include your training code.
 * __serve__: The wrapper that starts the inference server. In most cases, you can use this file as-is.