Change flow of MM overviews

Signed-off-by: Rafael Vasquez <[email protected]>
kserve · Oct 5, 2023 · 1fdbb02 · 1fdbb02
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 The ModelMesh framework is a mature, general-purpose model serving management/routing layer designed for high-scale, high-density and frequently-changing model use cases. It works with existing or custom-built model servers and acts as a distributed LRU cache for serving runtime models.
 
-See these [charts](https://github.com/kserve/modelmesh/files/8854091/modelmesh-jun2022.pdf) for more information on supported features and design details.
-
 For full Kubernetes-based deployment and management of ModelMesh clusters and models, see the [ModelMesh Serving](https://github.com/kserve/modelmesh-serving) repo. This includes a separate controller and provides K8s custom resource based management of ServingRuntimes and InferenceServices along with common, abstracted handling of model repository storage and ready-to-use integrations with some existing OSS model servers.
 
+For more information on supported features and design details, see [these charts](https://github.com/kserve/modelmesh/files/8854091/modelmesh-jun2022.pdf).
+
 ## Get Started
 
 To get started with the ModelMesh framework, check out [this guide](/docs/overview.md).
diff --git a/docs/overview.md b/docs/overview.md
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 # Overview
 
-ModelMesh is a distributed LRU cache for serving runtime models. 
-
-See these [charts](https://github.com/kserve/modelmesh/files/8854091/modelmesh-jun2022.pdf) for more information on supported features and design details.
+ModelMesh is a mature, general-purpose model serving management/routing layer designed for high-scale, high-density and frequently-changing model use cases. It works with existing or custom-built model servers and acts as a distributed LRU cache for serving runtime models.
 
 For full Kubernetes-based deployment and management of ModelMesh clusters and models, see the [ModelMesh Serving](https://github.com/kserve/modelmesh-serving) repo. This includes a separate controller and provides K8s custom resource based management of ServingRuntimes and InferenceServices along with common, abstracted handling of model repository storage and ready-to-use integrations with some existing OSS model servers.
 
+For more information on supported features and design details, see [these charts](https://github.com/kserve/modelmesh/files/8854091/modelmesh-jun2022.pdf).
+
 ## What is a model?
 
 In ModelMesh, a **model** refers to an abstraction of machine learning models. It is not aware of the underlying model format. There are two model types: model (regular) and vmodel. Regular models in ModelMesh are assumed and required to be immutable. VModels add a layer of indirection in front of the immutable models. See [VModels Reference](/docs/vmodels.md) for further reading.