diff --git a/docs/README.md b/docs/README.md
index a5df66d1f..5ba4f6966 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -4,12 +4,12 @@
 
 * Intel-Optimized TensorFlow (*coming soon*)
 * Intel-Optimized TensorFlow Serving:
-    * [Installation Guide](general/tensorflow_serving/InstallationGuide.md)
-    * [General Best Practices](general/tensorflow_serving/GeneralBestPractices.md)
+    * [Installation Guide](/docs/general/tensorflow_serving/InstallationGuide.md)
+    * [General Best Practices](/docs/general/tensorflow_serving/GeneralBestPractices.md)
 ## Tutorials by Use Case
 
 * Intel-Optimized TensorFlow (*coming soon*)
 * Intel-Optimized TensorFlow Serving:
-    * [Image Recognition](image_recognition/tensorflow_serving/Tutorial.md) (ResNet50 and InceptionV3)
+    * [Image Recognition](/docs/image_recognition/tensorflow_serving/Tutorial.md) (ResNet50 and InceptionV3)
     * Object Detection (*coming soon*)
       
\ No newline at end of file
diff --git a/docs/general/tensorflow_serving/GeneralBestPractices.md b/docs/general/tensorflow_serving/GeneralBestPractices.md
index d8aba6474..7617b3e07 100644
--- a/docs/general/tensorflow_serving/GeneralBestPractices.md
+++ b/docs/general/tensorflow_serving/GeneralBestPractices.md
@@ -86,5 +86,5 @@ Figure 1. NCHW format
     - ***TENSORFLOW_SESSION_PARALLELISM*** = *num_physical_cores*/4
 3. Use NCHW data format for images
 4. See one of the hands-on tutorials for an advanced walkthrough of your use case:
-   * [Image Recognition](https://github.com/NervanaSystems/intel-models/blob/master/docs/image_recognition/tensorflow_serving/Tutorial.md) (ResNet50 and InceptionV3)
+   * [Image Recognition](/docs/image_recognition/tensorflow_serving/Tutorial.md) (ResNet50 and InceptionV3)
    * Object Detection (*coming soon*)
\ No newline at end of file
diff --git a/docs/image_recognition/tensorflow_serving/Tutorial.md b/docs/image_recognition/tensorflow_serving/Tutorial.md
index 3d85b9ec0..13c62ab22 100644
--- a/docs/image_recognition/tensorflow_serving/Tutorial.md
+++ b/docs/image_recognition/tensorflow_serving/Tutorial.md
@@ -11,12 +11,12 @@ It also provides sample code that you can use to get your optimized TensorFlow m
 ## Prerequisites
 
 This tutorial assumes you have already:
-* [Installed TensorFlow Serving](https://github.com/NervanaSystems/intel-models/blob/master/docs/general/tensorflow_serving/InstallationGuide.md)
-* Read and understood the [General Best Practices](https://github.com/NervanaSystems/intel-models/blob/master/docs/general/tensorflow_serving/GeneralBestPractices.md),
+* [Installed TensorFlow Serving](/docs/general/tensorflow_serving/InstallationGuide.md)
+* Read and understood the [General Best Practices](/docs/general/tensorflow_serving/GeneralBestPractices.md),
   especially these sections:
    * **Performance Metrics**
    * **TensorFlow Serving Configuration Settings**
-* Ran an example end-to-end using a GRPC client, such as the one in the [Installation Guide](https://github.com/NervanaSystems/intel-models/blob/master/docs/general/tensorflow_serving/InstallationGuide.md)
+* Ran an example end-to-end using a GRPC client, such as the one in the [Installation Guide](/docs/general/tensorflow_serving/InstallationGuide.md#option-2-using-grpc-this-is-the-fastest-method-but-the-client-has-more-dependencies)
   
 ## Background
 
@@ -31,8 +31,8 @@ Tuning TensorFlow Serving to take full advantage of your hardware for image reco
 ## Hands-on Tutorial - ResNet50 or InceptionV3
 
 For steps 1 and 2, refer to the Intel Model Zoo FP32 benchmarks:
-* [ResNet50 README](https://github.com/NervanaSystems/intel-models/tree/master/benchmarks/image_recognition/tensorflow/resnet50#fp32-inference-instructions)
-* [InceptionV3 README](https://github.com/NervanaSystems/intel-models/tree/master/benchmarks/image_recognition/tensorflow/inceptionv3#fp32-inference-instructions)
+* [ResNet50 README](/benchmarks/image_recognition/tensorflow/resnet50#fp32-inference-instructions)
+* [InceptionV3 README](/benchmarks/image_recognition/tensorflow/inceptionv3#fp32-inference-instructions)
 
 1. **Download the Model**: Download and extract the ResNet50 or InceptionV3 pre-trained model (FP32), using the instructions in one of the READMEs above.
 
@@ -81,7 +81,7 @@ For steps 1 and 2, refer to the Intel Model Zoo FP32 benchmarks:
    For example, for a machine with `Core(s) per socket: 28` and `Socket(s): 2`, *num_physical_cores* = 28 * 2 = 56.
 
 7. **Start the server**: Now let's start up the TensorFlow model server. To optimize overall performance, use the following recommended settings from the
-   [General Best Practices](https://github.com/NervanaSystems/intel-models/blob/master/docs/general/tensorflow_serving/GeneralBestPractices.md):
+   [General Best Practices](/docs/general/tensorflow_serving/GeneralBestPractices.md):
    * OMP_NUM_THREADS = *num_physical_cores*
    * TENSORFLOW_SESSION_PARALLELISM = *num_physical_cores*/4