Update 2022-07-21-KServe-0.9-release.md

Signed-off-by: Dan Sun <[email protected]>

Author: yuzisun

docs/blog/articles/2022-07-21-KServe-0.9-release.md

@@ -17,8 +17,8 @@ KServe has the unique strength to build the distributed inference graph with its
 The graph router is deployed behind an HTTP endpoint and can be scaled dynamically based on request volume. The InferenceGraph supports four different types of routing nodes: **Sequence**, **Switch**, **Ensemble**, **Splitter**.
 
 
-- **Sequence Node**: It allows users to define multiple Steps with InferenceServices or Nodes as routing targets in a sequence. The Steps are executed in sequence and the request/response from the previous step and be passed to the next step as input based on configuration.
-- **Switch Node**: It allows users to define routing conditions and select a step to execute if it matches the condition. The response is returned as soon as it finds the first step that matches the condition. If no condition is matched, the graph returns the original request.
+- **Sequence Node**: It allows users to define multiple `Steps` with `InferenceServices` or `Nodes` as routing targets in a sequence. The `Steps` are executed in sequence and the request/response from the previous step and be passed to the next step as input based on configuration.
+- **Switch Node**: It allows users to define routing conditions and select a `Step` to execute if it matches the condition. The response is returned as soon as it finds the first step that matches the condition. If no condition is matched, the graph returns the original request.
 - **Ensemble Node**: A model ensemble requires scoring each model separately and then combines the results into a single prediction response. You can then use different combination methods to produce the final result. Multiple classification trees, for example, are commonly combined using a "majority vote" method. Multiple regression trees are often combined using various averaging techniques.
 - **Splitter Node**: It allows users to split the traffic to multiple targets using a weighted distribution.
 
@@ -63,7 +63,8 @@ spec:
         data: $request
         condition: "[@this].#(predictions.0==\"dog\")"
 ```
-Currently the `Serverless` deployment mode is supported with inference graphs. You can try it out following the [tutorial](https://kserve.github.io/website/master/modelserving/inference_graph/image_pipeline/) here.
+
+Currently `InferenceGraph` is supported with the `Serverless` deployment mode. You can try it out following the [tutorial](https://kserve.github.io/website/master/modelserving/inference_graph/image_pipeline/).
 
 
 ## InferenceService API for ModelMesh
@@ -83,7 +84,7 @@ storage:
   parameters: # Parameters to override the default values inside the common secret.
     bucket: example-models
 ```
-Learn more [here](https://kserve.github.io/website/master/modelserving/inference_graph/image_pipeline/).
+Learn more [here](https://github.com/kserve/kserve/tree/release-0.9/docs/samples/storage/storageSpec).
 
 
 
@@ -122,10 +123,10 @@ spec:
 
 ## Other New Features:
 
-- Support serving MLFlow model via MLServer serving runtime.
-- Support unified autoscaling target and metric fields for InferenceService components with both Serverless and RawDeployment mode.
-- Support InferenceService ingress class and url domain template configuration for RawDeployment mode.
-- ModelMesh now has a default OpenVINO Model Server ServingRuntime.
+- Support [serving MLFlow model format](https://kserve.github.io/website/0.9/modelserving/v1beta1/mlflow/v2/) via MLServer serving runtime.
+- Support [unified autoscaling target and metric fields](https://kserve.github.io/website/0.9/modelserving/autoscaling/autoscaling/) for InferenceService components with both Serverless and RawDeployment mode.
+- Support [InferenceService ingress class and url domain template configuration](https://kserve.github.io/website/0.9/admin/kubernetes_deployment/) for RawDeployment mode.
+- ModelMesh now has a default [OpenVINO Model Server](https://github.com/openvinotoolkit/model_server) ServingRuntime.
 
 
 ## What’s Changed?
@@ -136,14 +137,13 @@ spec:
 - Update MLServer serving runtime to 1.0.0
 
 
-
 ## Join the community
 
 - Visit our [Website](https://kserve.github.io/website/) or [GitHub](https://github.com/kserve)
 - Join the Slack ([#kserve](https://kubeflow.slack.com/join/shared_invite/zt-n73pfj05-l206djXlXk5qdQKs4o1Zkg#/))
 - Attend our community meeting by subscribing to the [KServe calendar](https://wiki.lfaidata.foundation/display/kserve/calendars).
 - View our [community github repository](https://github.com/kserve/community) to learn how to make contributions. We are excited to work with you to make KServe better and promote its adoption!
 
-Thank you for using or checking out KServe!
+Thank you for contributing or checking out KServe!
 
 – The KServe Working Group