diff --git a/Using-TensorFlow-Lite-for-the-Ultimate-Goal-Challenge.md b/Using-TensorFlow-Lite-for-the-Ultimate-Goal-Challenge.md
index 265ca23..f2b5e16 100644
--- a/Using-TensorFlow-Lite-for-the-Ultimate-Goal-Challenge.md
+++ b/Using-TensorFlow-Lite-for-the-Ultimate-Goal-Challenge.md
@@ -24,7 +24,7 @@ Click on the following links to learn more about these sample Op Modes.
 * [Java Tensor Flow Object Detection Example](Java-Sample-TensorFlow-Object-Detection-Op-Mode)
 
 ### Using a Custom Inference Model
-Teams have the option of using a customer inference model with the FIRST Tech Challenge software.  For example, some teams prefer to use the [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) to create an enhanced model of the game elements, or they might want to create a custom model to detect other entirely different objects.  Other teams might also want to use an available pre-trained model to build a robot that can detect common everyday objects (for demo or outreach purposes, for example).
+Teams have the option of using a custom inference model with the FIRST Tech Challenge software.  For example, some teams prefer to use the [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) to create an enhanced model of the game elements, or they might want to create a custom model to detect other entirely different objects.  Other teams might also want to use an available pre-trained model to build a robot that can detect common everyday objects (for demo or outreach purposes, for example).
 
 The FTC software includes sample op modes (Blocks and Java versions) that demonstrate how to use a custom inference model: