Address comments

Signed-off-by: Dan Sun <dsun20@bloomberg.net>

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

@@ -5,19 +5,19 @@ improving metrics for supported serving runtimes and increasing support coverage
 
 ## 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/) to see how you can enable other ingress options supported by Knative with Serverless mode.
+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/).
 
 ## KServe Telemetry for Serving Runtimes
 
-Prometheus metrics are now available for supported serving runtimes including custom python runtimes,
-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 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`.
 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
 
-In KServe 0.10, we have added support for Open(v2) inference protocol for KServe custom runtimes, 
-now you can enable v2 REST/gRPC for both custom transformer and predictor with images built by implementing KServe Python SDK API.
+In KServe 0.10, we have added support for Open(v2) inference protocol for KServe custom runtimes.
+Now, you can enable v2 REST/gRPC for both custom transformer and predictor with images built by implementing KServe Python SDK API.
 gRPC enables high performance inference data plane as it is built on top of HTTP/2 and binary data transportation which is more efficient to send over the wire compared to REST.
 Please see the detailed example for [transformer](https://kserve.github.io/website/0.10/modelserving/v1beta1/transformer/torchserve_image_transformer/) and 
 [predictor](https://kserve.github.io/website/0.10/modelserving/v1beta1/custom/custom_model/).
@@ -61,7 +61,7 @@ You can use the same Python API type `InferRequest` and `InferResponse` for both
 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.
 
 
-Please check the following table for complete support matrix for ServingRuntimes and ModelFormats.
+Please check the following matrix for supported ServingRuntimes and ModelFormats.
 
 | Model Format        | v1           | v2 REST/gRPC | 
 | ------------------- |--------------| ----------------|
@@ -72,7 +72,7 @@ Please check the following table for complete support matrix for ServingRuntimes
 | Scikit-learn        | ✅ KServe       | ✅ MLServer |
 | XGBoost             | ✅ KServe       | ✅ MLServer |
 | LightGBM            | ✅ KServe       | ✅ MLServer |
-| MLFLow              | ❌              | ✅ MLServer |
+| MLFlow              | ❌              | ✅ MLServer |
 | Custom              | ✅ KServe       | ✅ KServe |
 
 
@@ -90,12 +90,11 @@ for multiple architectures: `ppc64le`, `arm64`, `amd64`, `s390x`.
 - Support Azure Blobs with [managed identity](https://docs.microsoft.com/en-us/azure/active-directory/managed-identities-azure-resources/how-manage-user-assigned-managed-identities?pivots=identity-mi-methods-azcli).
 
 ## 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.
 
-- Support for TorchServe ServingRuntime.
-- Support v2 inference protocol for OpenVINO ServingRuntime.
-- `ClusterServingRuntime` support for ModelMesh which now works in the same way as KServe.
-- Support passing labels and annotations from `ServingRuntimePodSpec` to ModelMesh Pods.
-- Support for `ImagePullSecrets` on ServingRuntime spec.
+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.
+
+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.
 
 
 ## Other Changes: