WtD fixit: Readability improvements for KFServing (#1399)

* WtD fixit: Readability improvements for KFServing

Fixed: https://github.com/kubeflow/website/issues/1396

Additional note: It's not clear what the "Examples" are of. I'm not familiar enough with the software to update the heading to something more appropriate, though.

* Update kfserving.md

* Update kfserving.md

* Update kfserving.md

content/docs/components/serving/kfserving.md

 +++
 title = "KFServing"
-description = "Model Serving using KFServing"
+description = "Model serving using KFServing"
 weight = 2
 +++
 
-KFServing enables Serverless inferencing on Kubernetes and provides performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX to solve production model serving use cases
+KFServing enables serverless inferencing on Kubernetes and provides performant, high abstraction interfaces for common machine learning (ML) frameworks like TensorFlow, XGBoost, scikit-learn, PyTorch, and ONNX to solve production model serving use cases.
 
-KFServing:
+You can use KFServing to do the following:
 
-* Provides a Kubernetes [Custom Resource Definition](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) for serving ML models on arbitrary frameworks.
+* Provide a Kubernetes [Custom Resource Definition](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) for serving ML models on arbitrary frameworks.
 
-* Encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU autoscaling, scale to zero, and canary rollouts to your ML deployments
+* Encapsulate the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU autoscaling, scale to zero, and canary rollouts to your ML deployments.
 
-* Enables a simple, pluggable, and complete story for your production ML inference server by providing prediction, pre-processing, post-processing and explainability out of the box.
+* Enable a simple, pluggable, and complete story for your production ML inference server by providing prediction, pre-processing, post-processing and explainability out of the box.
 
-* Is evolving with strong community contributions, and has a Technical Steering Committee driven by Google, IBM, Microsoft, Seldon, and Bloomberg
+Our strong community contributions help KFServing to grow. We have a Technical Steering Committee driven by Google, IBM, Microsoft, Seldon, and Bloomberg. [Browse the KFServing GitHub repo](https://github.com/kubeflow/kfserving) to give us feedback!
 
-Please browse through the [KFServing GitHub repo](https://github.com/kubeflow/kfserving) and give us feedback! 
-
-## Installation with Kubeflow v0.7 ##
-KFServing can be installed with Kubeflow v0.7, and KFServing kustomize installation files are [located in the manifests repo](https://github.com/kubeflow/manifests/tree/master/kfserving).
+## Install with Kubeflow
+KFServing works with Kubeflow 0.7. Kustomize installation files are [located in the manifests repo](https://github.com/kubeflow/manifests/tree/master/kfserving).
 
 <img src="../kfserving.png" alt="KFServing">
 
 ## Examples
-* [Tensorflow](https://github.com/kubeflow/kfserving/tree/master/docs/samples/tensorflow)
+* [TensorFlow](https://github.com/kubeflow/kfserving/tree/master/docs/samples/tensorflow)
 * [PyTorch](https://github.com/kubeflow/kfserving/tree/master/docs/samples/pytorch)
 * [XGBoost](https://github.com/kubeflow/kfserving/tree/master/docs/samples/xgboost)
-* [Scikit-Learn](https://github.com/kubeflow/kfserving/tree/master/docs/samples/sklearn)
+* [scikit-learn](https://github.com/kubeflow/kfserving/tree/master/docs/samples/sklearn)
 * [ONNX](https://github.com/kubeflow/kfserving/tree/master/docs/samples/onnx)
 * [Custom](https://github.com/kubeflow/kfserving/tree/master/docs/samples/custom)
 * [TensorRT](https://github.com/kubeflow/kfserving/tree/master/docs/samples/tensorrt)
@@ -36,24 +34,24 @@ KFServing can be installed with Kubeflow v0.7, and KFServing kustomize installat
 * [Pipelines](https://github.com/kubeflow/kfserving/tree/master/docs/samples/pipelines)
 * [Explainability](https://github.com/kubeflow/kfserving/tree/master/docs/samples/explanation/alibi)
 
-## Sample Notebooks
-* [SDK Client](https://github.com/kubeflow/kfserving/blob/master/docs/samples/client/kfserving_sdk_sample.ipynb)
-* [Transformer (Pre/Post Processing)](https://github.com/kubeflow/kfserving/blob/master/docs/samples/transformer/image_transformer/kfserving_sdk_transformer.ipynb)
+## Sample notebooks
+* [SDK client](https://github.com/kubeflow/kfserving/blob/master/docs/samples/client/kfserving_sdk_sample.ipynb)
+* [Transformer (pre/post processing)](https://github.com/kubeflow/kfserving/blob/master/docs/samples/transformer/image_transformer/kfserving_sdk_transformer.ipynb)
 * [ONNX](https://github.com/kubeflow/kfserving/blob/master/docs/samples/onnx/mosaic-onnx.ipynb)
 
-Please be on the lookout, we are constantly adding [more examples](https://github.com/kubeflow/kfserving/tree/master/docs/samples/) about available features
+We frequently add examples to our [GitHub repo](https://github.com/kubeflow/kfserving/tree/master/docs/samples/).
 
-## Learn More
-* Join our [Working Group](https://groups.google.com/forum/#!forum/kfserving) for meeting invites and discussion.
+## Learn more
+* Join our [working group](https://groups.google.com/forum/#!forum/kfserving) for meeting invitations and discussion.
 * [Read the docs](https://github.com/kubeflow/kfserving/tree/master/docs).
 * [API docs](https://github.com/kubeflow/kfserving/tree/master/docs/apis/README.md).
 * [Roadmap](https://github.com/kubeflow/kfserving/tree/master/ROADMAP.md).
 * [KFServing 101 slides](https://drive.google.com/file/d/16oqz6dhY5BR0u74pi9mDThU97Np__AFb/view).
 
 ## Prerequisites
-KNative Serving (v0.8.0 +) and Istio (v1.1.7+) should be available on Kubernetes Cluster.
+Knative Serving (v0.8.0 +) and Istio (v1.1.7+) should be available on your Kubernetes cluster.
 
-If you want to install Knative, you may find this [installation instruction](https://github.com/kubeflow/kfserving/blob/master/docs/DEVELOPER_GUIDE.md#install-knative-on-a-kubernetes-cluster) useful.
+Read more about [installing Knative on a Kubernetes cluster](https://github.com/kubeflow/kfserving/blob/master/docs/DEVELOPER_GUIDE.md#install-knative-on-a-kubernetes-cluster).
 
 ## KFServing installation using kubectl
 ```
@@ -62,12 +60,11 @@ kubectl apply -f ./install/$TAG/kfserving.yaml
 ```
 
 ## Use
-* Install the SDK
+1. Install the SDK.
 ```
 pip install kfserving
 ```
-* Follow the [example](https://github.com/kubeflow/kfserving/blob/master/docs/samples/client/kfserving_sdk_sample.ipynb) to use the KFServing SDK to create, patch, rollout and delete a KFService instance.
+1. [Follow the example](https://github.com/kubeflow/kfserving/blob/master/docs/samples/client/kfserving_sdk_sample.ipynb) to use the KFServing SDK to create, patch, roll out, and delete a KFServing instance.
 
 ## Contribute
 * [Developer guide](https://github.com/kubeflow/kfserving/tree/master/docs/DEVELOPER_GUIDE.md).
-