Serving Comparison Page (#1405)

* Add Model Serving Overview Page

* Add Model Serving Overview Page

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content/docs/components/serving/overview.md

++++
+title = "Overview"
+description = "Model serving overview"
+weight = 1
++++
+
+## Multi-framework serving
+
+Kubeflow provides two supported open source model serving systems that allow multi-framework model serving: KFServing and Seldon Core. You should choose the framework that best supports your model serving requirements.  A rough comparison between KFServing and Seldon Core is shown below:
+
+| Feature        | sub-feature    | KFServing | Seldon |
+|----------------|----------------|  :--:     |  :--:  |
+| Framework      | TensorFlow     | [x](https://github.com/kubeflow/kfserving/tree/master/docs/samples/tensorflow) | [x](https://docs.seldon.io/projects/seldon-core/en/latest/servers/tensorflow.html)  |
+|                | XGBoost        | [x](https://github.com/kubeflow/kfserving/tree/master/docs/samples/xgboost) | [x](https://docs.seldon.io/projects/seldon-core/en/latest/servers/xgboost.html) |
+|                | scikit-learn        | [x](https://github.com/kubeflow/kfserving/tree/master/docs/samples/sklearn) | [x](https://docs.seldon.io/projects/seldon-core/en/latest/servers/sklearn.html) |
+|                | NVIDIA TensorRT Inference Server    | [x](https://github.com/kubeflow/kfserving/tree/master/docs/samples/tensorrt) | [x](https://docs.seldon.io/projects/seldon-core/en/latest/examples/nvidia_mnist.html) |
+|                | ONNX           | [x](https://docs.seldon.io/projects/seldon-core/en/latest/examples/onnx_resnet.html) | [x](https://docs.seldon.io/projects/seldon-core/en/latest/examples/onnx_resnet.html) |
+|                | PyTorch        | [x](https://github.com/kubeflow/kfserving/tree/master/docs/samples/pytorch) | [x](https://www.kubeflow.org/docs/components/serving/pytorchserving/) |
+| Graph          | Transformers   | [x](https://github.com/kubeflow/kfserving/blob/master/docs/samples/transformer/image_transformer/kfserving_sdk_transformer.ipynb) | [x](https://docs.seldon.io/projects/seldon-core/en/latest/examples/transformer_spam_model.html)
+|                | Combiners       | Roadmap | [x](https://docs.seldon.io/projects/seldon-core/en/latest/examples/openvino_ensemble.html) |
+|                | Routers incl ([MAB](https://en.wikipedia.org/wiki/Multi-armed_bandit))         | Roadmap | [x](https://docs.seldon.io/projects/seldon-core/en/latest/analytics/routers.html) |
+| Analytics      | Explanations   | [x](https://github.com/kubeflow/kfserving/tree/master/docs/samples/explanation/alibi) | [x](https://docs.seldon.io/projects/seldon-core/en/latest/analytics/explainers.html) |
+| Scaling        | Knative        | [x](https://github.com/kubeflow/kfserving/tree/master/docs/samples/autoscaling) | |
+|                | GPU AutoScaling| [x](https://github.com/kubeflow/kfserving/tree/master/docs/samples/autoscaling) | |
+|                | HPA            |  x | [x](https://docs.seldon.io/projects/seldon-core/en/latest/graph/autoscaling.html) |
+| Custom         |  Container     | [x](https://github.com/kubeflow/kfserving/tree/master/docs/samples/custom) | [x](https://docs.seldon.io/projects/seldon-core/en/latest/wrappers/README.html) |
+|                | Language Wrappers | | [python](https://docs.seldon.io/projects/seldon-core/en/latest/python/index.html), [java](https://docs.seldon.io/projects/seldon-core/en/latest/java/README.html), [R](https://docs.seldon.io/projects/seldon-core/en/latest/R/README.html) |
+|                | Multi-Container | | [x](https://docs.seldon.io/projects/seldon-core/en/latest/graph/inference-graph.html) |
+| Rollout        | Canary         | [x](https://github.com/kubeflow/kfserving/tree/master/docs/samples/rollouts) | [x](https://docs.seldon.io/projects/seldon-core/en/latest/examples/istio_canary.html) |
+|                | Shadow          | | x |
+| istio          |                 | x | x |
+
+
+Notes:
+
+   * Both projects share technology including Explainability (via [Seldon Alibi Explain](https://github.com/SeldonIO/alibi)) and Payload Logging amongst other areas.
+   * A commercial product [Seldon Deploy](https://www.seldon.io/tech/products/deploy/) is available from Seldon that supports both KFServing and Seldon in production.
+   * KFServing is part of the Kubeflow project ecosystem. Seldon is an external project supported within Kubeflow.
+
+For further information:
+
+ * KFServing:
+   * [Github Repo](https://github.com/kubeflow/kfserving)
+   * [Kubeflow Documentation](https://www.kubeflow.org/docs/components/serving/kfserving/)
+   * [Community](https://www.kubeflow.org/docs/about/community/)
+ * Seldon
+   * [Github Repo](https://github.com/SeldonIO/seldon-core)
+   * [Kubeflow documentation](https://www.kubeflow.org/docs/components/serving/seldon/)
+   * [External Documentation](https://docs.seldon.io/projects/seldon-core/en/latest/)
+   * [Community](https://github.com/SeldonIO/seldon-core#community)
+
+
+## TensorFlow Serving
+
+For TensorFlow models you can use TensorFlow Serving for both [real-time](/docs/components/serving/tfserving_new) and [batch](/docs/components/serving/tfbatchpredict) prediction. Documentation is also provided on using [TensorFlow serving via Istio](/docs/components/serving/istio). However, if you are thinking of utlizing multiple frameworks we would suggest you use KFServing or Seldon Core as described above.
+
+## NVIDIA TensorRT Inference Server
+
+NVIDIA TensorRT Inference Server is a REST and GRPC service for deep-learning
+inferencing of TensorRT, TensorFlow and Caffe2 models. The server is
+optimized deploy machine and deep learning algorithms on both GPUs and
+CPUs at scale.
+
+You can use [NVIDIA TensorRT Inference Server standalone](/docs/components/serving/trtinferenceserver) but we also recommend you look at using KFServing which includes support for NVIDIA TensorRT Inference Server.
+