diff --git a/advanced_functionality/data_distribution_types/data_distribution_types.ipynb b/advanced_functionality/data_distribution_types/data_distribution_types.ipynb
index 3c21fbd530..1b8ae57258 100644
--- a/advanced_functionality/data_distribution_types/data_distribution_types.ipynb
+++ b/advanced_functionality/data_distribution_types/data_distribution_types.ipynb
@@ -26,7 +26,7 @@
     "\n",
     "## Background\n",
     "\n",
-    "Amazon SageMaker makes it easy to train machine learning models across a large number of machines.  This a non-trivial process, but Amazon SageMaker Algorithms and pre-bruilt MXNet and TensorFlow containers hide most of the complexity from you.  Nevertheless, there are decisions on how a user structures their data which will have an implication on how the distributed training is carried out.  This notebook will walk through details on setting up your data to take full advantage of distributed processing.\n",
+    "Amazon SageMaker makes it easy to train machine learning models across a large number of machines.  This a non-trivial process, but Amazon SageMaker Algorithms and pre-built MXNet and TensorFlow containers hide most of the complexity from you.  Nevertheless, there are decisions on how a user structures their data which will have an implication on how the distributed training is carried out.  This notebook will walk through details on setting up your data to take full advantage of distributed processing.\n",
     "\n",
     "---\n",
     "# Setup\n",