Moving systems examples to Merlin (#1034)

* moving systems examples to Merlin

* serve ranking models

* move tests and update ranking_serving test

* update nb

* update paths and rename nb

* fix typo

* move traditional-ml out of ranking

examples/ranking/README.md

@@ -0,0 +1,47 @@
+# Training and Deploying Ranking models with Merlin
+
+Ranking models are probably the most common use case in recommender systems. The examples under this folder are designed to demonstrate how to build, train and evaluate a ranking model (e.g. DLRM) using Merlin Models and deploy on [Triton Inference Server](https://github.com/triton-inference-server/server) with Merlin Systems. Currently we support models built with TensorFlow framework, and traditional-ml models like XGBoost and python-based models with implicit datasets. Examples built with PyTorch framework are being developed and will be added here soon. 
+
+To learn more about ranking models, please visit this documentation [page](https://nvidia-merlin.github.io/Merlin/stable/guide/recommender_models.html#).
+
+## Running the Example Notebooks
+
+Docker containers are available from the NVIDIA GPU Cloud.
+We use the latest stable version of the [merlin-tensorflow](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-tensorflow/tags) container to run the example notebooks. To run the example notebooks using Docker containers, perform the following steps:
+
+
+1. Pull and start the container by running the following command:
+
+   ```shell
+   docker run --gpus all --rm -it \
+     -p 8888:8888 -p 8797:8787 -p 8796:8786 --ipc=host \
+     nvcr.io/nvidia/merlin/merlin-tensorflow:23.XX /bin/bash
+   ```
+
+   > You can find the release tags and more information on the [merlin-tensorflow](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-tensorflow) container page.
+
+   The container opens a shell when the run command execution is completed.
+   Your shell prompt should look similar to the following example:
+
+   ```shell
+   root@2efa5b50b909:
+   ```
+
+2. Start the JupyterLab server by running the following command:
+
+   ```shell
+   jupyter-lab --allow-root --ip='0.0.0.0'
+   ```
+
+   View the messages in your terminal to identify the URL for JupyterLab.
+   The messages in your terminal show similar lines to the following example:
+
+   ```shell
+   Or copy and paste one of these URLs:
+   http://2efa5b50b909:8888/lab?token=9b537d1fda9e4e9cadc673ba2a472e247deee69a6229ff8d
+   or http://127.0.0.1:8888/lab?token=9b537d1fda9e4e9cadc673ba2a472e247deee69a6229ff8d
+   ```
+
+3. Open a browser and use the `127.0.0.1` URL provided in the messages by JupyterLab.
+
+4. After you log in to JupyterLab, navigate to the `/Merlin/examples/ranking` directory to try out the example notebooks.