Address comments

docs/blog/articles/2023-02-05-KServe-0.10-release.md

@@ -1,17 +1,17 @@
 # Announcing: KServe v0.10.0
 
-We are excited to announce KServe 0.10 release, in this release we have been focusing on enabling more KServe networking setup options,
-improving metrics for supported serving runtimes and increasing support coverage for [Open(aka v2) inference protocol](https://kserve.github.io/website/0.10/modelserving/data_plane/v2_protocol/) for both KServe and ModelMesh.
+We are excited to announce KServe 0.10 release. In this release we have enabled more KServe networking options,
+improved metrics instruments for supported serving runtimes and increased support coverage for [Open(aka v2) inference protocol](https://kserve.github.io/website/0.10/modelserving/data_plane/v2_protocol/) for both standard and ModelMesh InferenceService.
 
 ## KServe Networking Options
 
-Istio is now optional for both `Serverless` and `RawDeployment` mode, please see the [alternative networking guide](https://kserve.github.io/website/0.10/admin/serverless/kourier_networking/) for how you can enable other ingress options supported by Knative with Serverless mode.
-For Istio users, if you want to turn on full service mesh mode to secure inference services with mutual TLS and enable the traffic policies, please read the [service mesh setup guideline](https://kserve.github.io/website/0.10/admin/serverless/servicemesh/).
+Istio is now optional for both `Serverless` and `RawDeployment` mode. Please see the [alternative networking guide](https://kserve.github.io/website/0.10/admin/serverless/kourier_networking/) for how you can enable other ingress options supported by Knative with Serverless mode.
+For Istio users, if you want to turn on full service mesh mode to secure InferenceService with mutual TLS and enable the traffic policies, please read the [service mesh setup guideline](https://kserve.github.io/website/0.10/admin/serverless/servicemesh/).
 
 ## KServe Telemetry for Serving Runtimes
 
-We have instrumented additional latency metrics in KServe python serving runtimes for `preprocess`, `predict` and `postprocess` handlers.
-In Serverless mode we have extended Knative queue-proxy to enable metrics aggregation for both metrics exposed in `queue-proxy` and `kserve-container`.
+We have instrumented additional latency metrics in KServe Python ServingRuntimes for `preprocess`, `predict` and `postprocess` handlers.
+In Serverless mode we have extended Knative `queue-proxy` to enable metrics aggregation for both metrics exposed in `queue-proxy` and `kserve-container` from each `ServingRuntime`.
 Please read the [prometheus metrics setup guideline](https://kserve.github.io/website/0.10/modelserving/observability/prometheus_metrics/) for how to enable the metrics scraping and aggregations.
 
 ## Open(v2) Inference Protocol Support Coverage
@@ -57,8 +57,11 @@ class CustomTransformer(Model):
         return infer_request
 ```
 
-You can use the same Python API type `InferRequest` and `InferResponse` for both REST and gRPC protocol, KServe handles the underlying decoding and encoding according to the protocol.
-New `headers` argument is added to the custom handlers to pass http/gRPC headers or metadata, you can also use this as context dict to pass data between handlers.
+You can use the same Python API type `InferRequest` and `InferResponse` for both REST and gRPC protocol. KServe handles the underlying decoding and encoding according to the protocol.
+
+!!! Warning
+    A new `headers` argument is added to the custom handlers to pass http/gRPC headers or other metadata. You can also use this as context dict to pass data between handlers.
+    If you have existing custom transformer or predictor, the `headers` argument is now required to add to the `preprocess`, `predict` and `postprocess` handlers.
 
 
 Please check the following matrix for supported ServingRuntimes and ModelFormats.
@@ -92,7 +95,7 @@ for multiple architectures: `ppc64le`, `arm64`, `amd64`, `s390x`.
 ## ModelMesh updates
 ModelMesh has continued to integrate itself as KServe's multi-model serving backend, introducing improvements and features that better align the two projects. For example, it now supports ClusterServingRuntimes, allowing use of cluster-scoped ServingRuntimes, originally introduced in KServe 0.8.
 
-Additionally, ModelMesh has introduced support for TorchServe enabling users to serve arbitrary PyTorch models (e.g. eager-mode) in the context of distributed-multi-model serving.
+Additionally, ModelMesh introduced support for TorchServe enabling users to serve arbitrary PyTorch models (e.g. eager-mode) in the context of distributed-multi-model serving.
 
 Other limitations have been addressed as well, such as adding support for BYTES/string type tensors when using the REST inference API for inference requests that require them.