update urls

advanced_functionality/parquet_to_recordio_protobuf/parquet_to_recordio_protobuf.ipynb

@@ -229,7 +229,7 @@
     "\n",
     "This example takes four to six minutes to complete. Majority of the time is spent provisioning hardware and loading the algorithm container since the dataset is small.\n",
     "\n",
-    "First, let's specify our containers.  Since we want this notebook to run in all 4 of Amazon SageMaker's regions, we'll create a small lookup.  More details on algorithm containers can be found in [AWS documentation](https://docs-aws.amazon.com/sagemaker/latest/dg/im-algo-docker-registry-paths.html)."
+    "First, let's specify our containers.  Since we want this notebook to run in all 4 of Amazon SageMaker's regions, we'll create a small lookup.  More details on algorithm containers can be found in [AWS documentation](https://docs-aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)."
    ]
   },
   {

advanced_functionality/scikit_bring_your_own/container/ReadMe.md

@@ -1,8 +1,8 @@
 # Bring-your-own Algorithm Sample
 
-This example shows how to package an algorithm for use with IM. We have chosen a simple [scikit-learn][skl] implementation of decision trees to illustrate the procedure.
+This example shows how to package an algorithm for use with SageMaker. We have chosen a simple [scikit-learn][skl] implementation of decision trees to illustrate the procedure.
 
-IM 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").
+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
 

advanced_functionality/scikit_bring_your_own/container/build_and_push.sh

@@ -1,7 +1,7 @@
 #!/usr/bin/env bash
 
 # This script shows how to build the Docker image and push it to ECR to be ready for use
-# by IM.
+# by SageMaker.
 
 # The argument to this script is the image name. This will be used as the image on the local
 # machine and combined with the account and region to form the repository name for ECR.

introduction_to_amazon_algorithms/factorization_machines_mnist/factorization_machines_mnist.ipynb

@@ -209,7 +209,7 @@
     "\n",
     "Again, we'll use the Amazon SageMaker Python SDK to kick off training and monitor status until it is completed.  In this example that takes between 7 and 11 minutes.  Despite the dataset being small, provisioning hardware and loading the algorithm container take time upfront.\n",
     "\n",
-    "First, let's specify our containers.  Since we want this notebook to run in all 4 of Amazon SageMaker's regions, we'll create a small lookup.  More details on algorithm containers can be found in [AWS documentation](https://docs-aws.amazon.com/sagemaker/latest/dg/im-algo-docker-registry-paths.html)."
+    "First, let's specify our containers.  Since we want this notebook to run in all 4 of Amazon SageMaker's regions, we'll create a small lookup.  More details on algorithm containers can be found in [AWS documentation](https://docs-aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)."
    ]
   },
   {

introduction_to_amazon_algorithms/linear_learner_mnist/linear_learner_mnist.ipynb

@@ -207,7 +207,7 @@
     "\n",
     "Again, we'll use the Amazon SageMaker Python SDK to kick off training, and monitor status until it is completed.  In this example that takes between 7 and 11 minutes.  Despite the dataset being small, provisioning hardware and loading the algorithm container take time upfront.\n",
     "\n",
-    "First, let's specify our containers.  Since we want this notebook to run in all 4 of Amazon SageMaker's regions, we'll create a small lookup.  More details on algorithm containers can be found in [AWS documentation](https://docs-aws.amazon.com/sagemaker/latest/dg/im-algo-docker-registry-paths.html)."
+    "First, let's specify our containers.  Since we want this notebook to run in all 4 of Amazon SageMaker's regions, we'll create a small lookup.  More details on algorithm containers can be found in [AWS documentation](https://docs-aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)."
    ]
   },
   {

introduction_to_amazon_algorithms/pca_mnist/pca_mnist.ipynb

@@ -209,7 +209,7 @@
     "\n",
     "Again, we'll use the Amazon SageMaker Python SDK to kick off training, and monitor status until it is completed.  In this example that takes between 7 and 11 minutes.  Despite the dataset being small, provisioning hardware and loading the algorithm container take time upfront.\n",
     "\n",
-    "First, let's specify our containers.  Since we want this notebook to run in all 4 of Amazon SageMaker's regions, we'll create a small lookup.  More details on algorithm containers can be found in [AWS documentation](https://docs-aws.amazon.com/sagemaker/latest/dg/im-algo-docker-registry-paths.html)."
+    "First, let's specify our containers.  Since we want this notebook to run in all 4 of Amazon SageMaker's regions, we'll create a small lookup.  More details on algorithm containers can be found in [AWS documentation](https://docs-aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)."
    ]
   },
   {

introduction_to_applying_machine_learning/linear_time_series_forecast/linear_time_series_forecast.ipynb

@@ -301,7 +301,7 @@
     "---\n",
     "## Train\n",
     "\n",
-    "Now we can begin to specify our linear model.  First, let's specify the containers for the Linear Learner algorithm.  Since we want this notebook to run in all 4 of Amazon SageMaker's regions, we'll create a small lookup.  More details on algorithm containers can be found in [AWS documentation](https://docs-aws.amazon.com/sagemaker/latest/dg/im-algo-docker-registry-paths.html)."
+    "Now we can begin to specify our linear model.  First, let's specify the containers for the Linear Learner algorithm.  Since we want this notebook to run in all 4 of Amazon SageMaker's regions, we'll create a small lookup.  More details on algorithm containers can be found in [AWS documentation](https://docs-aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)."
    ]
   },
   {