:paper: updated readme for tensorflow serving example

Signed-off-by: Harshad Reddy Nalla <hnalla@redhat.com>

Author: harshad16

README.md

@@ -1,2 +1,36 @@
 # Tensorflow-model-server-RPM
-A Sample Example of Tensorflow model serving via centos6 based tensorflow model server rpm. 
+
+A Sample Example of Tensorflow model serving via centos6 based TensorFlow Model Server RPM and interact with it using TensorFlow Serving API.
+
+## Description
+
+Tensorflow and TensorFlow Serving API Wheels could be found at [AICoE Index](https://tensorflow.pypi.thoth-station.ninja)
+
+The TensorFlow Model Server RPM are generated from the tensorflow_model_server[Ex tf-r.1.14](https://github.com/AICoE/tensorflow-wheels/releases/tag/tensorflow_serving_api-r1.14-cpu-2019-08-08_154435)] binary released by [Red Hat AICoE](https://github.com/AICoE/tensorflow-wheels) along with the tensorflow serving api[[Ex tf-r.1.14](https://github.com/AICoE/tensorflow-wheels/releases/tag/tensorflow_serving_api-r1.14-cpu-2019-08-08_154435)].
+
+This is a sample example project to showcase how to use the tensorflow model server rpm for simple tensorflow model serving.
+
+## How to use
+
+It is a two step process:
+
+- one on the serving side, where the model would be developed, trained and tested, and then final been served via the TensorFlow Model Server.
+- second on the client side, where the model would be used via TensorFlow Serving API.
+
+A sample model is provided in the repo to serving.
+
+- tf_model.py : model to be served
+- client.py : request the model.
+
+ ### Serving side
+
+- Use the example fedora 28 based Dockerfile to create a container image, which installs tensorflow_model_server rpm and serves the provided valid model. `podman build --build-arg WHL=tensorflow-1.14.1-cp36-cp36m-linux_x86_64.whl --build-arg RPM=tensorflow-model-serving-1.14-1.0-1.x86_64.rpm -t tensoflow-serving -f Dockerfile .`
+
+- Run the container for serving the model via using tensorflow model server rpm. `podman run -p 9000:80 -it localhost/tensorflow-serving`
+
+### Client side
+
+- The example client side will using the tensorflow serving api to request the served model.
+- Example client side [code](client.py) and [tensorflow_serving_api](tensorflow_serving_api-1.14.0-py2.py3-none-any.whl) for installation are provided in the repo.
+- The following code could be used to request the model.<br>
+ `pipenv run python3 client.py`