diff --git a/docs/samples/pytorch/README.md b/docs/samples/pytorch/README.md
index 8e3d6b310e4..4eb4993ae45 100644
--- a/docs/samples/pytorch/README.md
+++ b/docs/samples/pytorch/README.md
@@ -28,7 +28,7 @@ Finished Training
 
 Then, we can run the PyTorch server using the trained model and test for predictions. Models can be on local filesystem, S3 compatible object storage, Azure Blob Storage, or Google Cloud Storage. 
 
-Note: Currently KFServing supports PyTorch models saved using [state_dict method]((https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-model-for-inference), PyTorch's recommended way of saving models for inference. The KFServing interface for PyTorch expects users to upload the model_class_file in same location as the PyTorch model, and accepts an optional model_class_name to be passed in as a runtime input. If model class name is not specified, we use 'PyTorchModel' as the default class name. The current interface may undergo changes as we evolve this to support PyTorch models saved using other methods as well.
+Note: Currently KFServing supports PyTorch models saved using [state_dict method](https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-model-for-inference), PyTorch's recommended way of saving models for inference. The KFServing interface for PyTorch expects users to upload the model_class_file in same location as the PyTorch model, and accepts an optional model_class_name to be passed in as a runtime input. If model class name is not specified, we use 'PyTorchModel' as the default class name. The current interface may undergo changes as we evolve this to support PyTorch models saved using other methods as well.
 
 ```shell
 python -m pytorchserver --model_dir ./ --model_name pytorchmodel --model_class_name Net
diff --git a/docs/samples/xgboost/README.md b/docs/samples/xgboost/README.md
index e59bdae30eb..605bd72ba34 100644
--- a/docs/samples/xgboost/README.md
+++ b/docs/samples/xgboost/README.md
@@ -1,4 +1,4 @@
-## Creating your own model and testing the SKLearn server.
+## Creating your own model and testing the XGBoost server.
 
 To test the XGBoost Server, first we need to generate a simple XGBoost model using Python. 
 
diff --git a/python/pytorchserver/README.md b/python/pytorchserver/README.md
index 6e2b1642fdf..3b826957e1a 100644
--- a/python/pytorchserver/README.md
+++ b/python/pytorchserver/README.md
@@ -113,7 +113,7 @@ An empty result will indicate success.
 
 You can build and publish your own image for development needs. Please ensure that you modify the kfservice files for PyTorch in the api directory to point to your own image.
 
-To build your own image, run
+To build your own image, navigate up one directory level to the `python` directory and run:
 
 ```bash
 docker build -t docker_user_name/pytorchserver -f pytorch.Dockerfile .
diff --git a/python/sklearnserver/README.md b/python/sklearnserver/README.md
index c180e8c25ec..8ee969b8671 100644
--- a/python/sklearnserver/README.md
+++ b/python/sklearnserver/README.md
@@ -107,12 +107,12 @@ An empty result will indicate success.
 
 ## Building your own Scikit-Learn Server Docker Image
 
-You can build and publish your own image for development needs. Please ensure that you modify the kfservice files for XGBoost in the api directory to point to your own image.
+You can build and publish your own image for development needs. Please ensure that you modify the kfservice files for Scikit-Learn in the api directory to point to your own image.
 
-To build your own image, run
+To build your own image, navigate up one directory level to the `python` directory and run:
 
 ```bash
-docker build -t animeshsingh/sklearnserver -f sklearn.Dockerfile .
+docker build -t docker_user_name/sklearnserver -f sklearn.Dockerfile .
 ```
 
 You should see an output similar to this
diff --git a/python/xgbserver/README.md b/python/xgbserver/README.md
index 5ec66a891a2..ec55cd9f3d8 100644
--- a/python/xgbserver/README.md
+++ b/python/xgbserver/README.md
@@ -112,10 +112,10 @@ An empty result will indicate success.
 
 You can build and publish your own image for development needs. Please ensure that you modify the kfservice files for XGBoost in the api directory to point to your own image.
 
-To build your own image, run
+To build your own image, navigate up one directory level to the `python` directory and run:
 
 ```bash
-docker build -t $USER/xgbserver -f xgb.Dockerfile .
+docker build -t docker_user_name/xgbserver -f xgb.Dockerfile .
 ```
 
 You should see an output similar to this