fix some typos (kubeflow#267)

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

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. 
 

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 .

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

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