diff --git a/tensorflow/lite/g3doc/models/image_classification/overview.md b/tensorflow/lite/g3doc/models/image_classification/overview.md
index 40ebbba052e..9bb4493cfe6 100644
--- a/tensorflow/lite/g3doc/models/image_classification/overview.md
+++ b/tensorflow/lite/g3doc/models/image_classification/overview.md
@@ -328,5 +328,5 @@ images for each of the new labels you wish to train.
 
 Learn how to perform transfer learning in the
 <a href="https://codelabs.developers.google.com/codelabs/recognize-flowers-with-tensorflow-on-android/#0">Recognize
-flowers with TensorFlow</a> codelab, or with the [model customization toolkit]
-(https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_customization/demo/image_classification.ipynb).
+flowers with TensorFlow</a> codelab, or with the
+[model maker toolkit](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_maker/demo/image_classification.ipynb).
diff --git a/tensorflow/lite/g3doc/models/text_classification/overview.md b/tensorflow/lite/g3doc/models/text_classification/overview.md
index 94960113d94..446d4625013 100644
--- a/tensorflow/lite/g3doc/models/text_classification/overview.md
+++ b/tensorflow/lite/g3doc/models/text_classification/overview.md
@@ -93,7 +93,7 @@ Performance benchmark numbers are generated with the tool
 ## Use your training dataset
 
 Follow this
-[tutorial](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_customization/demo/text_classification.ipynb)
+[tutorial](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_maker/demo/text_classification.ipynb)
 to apply the same technique used here to train a text classification model using
 your own datasets. With the right dataset, you can create a model for use cases
 such as document categorization or toxic comments detection.