Expanded docs for the Metadata component (kubeflow#1061)

* WIP Expanded docs for Metadata component.

* Added more about metadata.

* Addressed review comments.

content/docs/components/misc/metadata.md

@@ -4,43 +4,183 @@ description = "Tracking and managing metadata of machine learning workflows in K
 weight = 5
 +++
 
-The goal of the [Metadata](https://github.com/kubeflow/metadata) project is to help Kubeflow users understand and manage their machine learning workflows by tracking and managing the metadata of workflows.
+The goal of the [Metadata](https://github.com/kubeflow/metadata) project is to 
+help Kubeflow users understand and manage their machine learning (ML) workflows
+by tracking and managing the metadata that the workflows produce. 
 
-## Installation
+In this context, _metadata_ means information about executions (runs), models, 
+datasets, and other artifacts. _Artifacts_ are the files and objects that form 
+the inputs and outputs of the components in your ML workflow.
 
-The Metadata component is installed by default for Kubeflow versions >= 0.6.1.
+{{% alert title="Alpha version" color="warning" %}}
+This is an <b>alpha</b> release of the Metadata API. The next version of Kubeflow
+will introduce breaking changes. The development team is interested in any
+feedback you have while using the Metadata component, and in particular your
+feedback on any gaps in the functionality that the component offers.
+{{% /alert %}}
 
-If you want to install the latest version of the Metadata component or install it as an application in your Kubernetes cluster, you can follow these steps:
+## Installing the Metadata component
 
-1. Download the Kubeflow manifests repository.
-```
-git clone https://github.com/kubeflow/manifests
-```
+Kubeflow v0.6.1 and later versions install the Metadata component by default.
+You can skip this section if you are running Kubeflow v0.6.1 or later.
 
-2. Run the following commands in the manifest repository to deploy services of the Metadata component.
-```
-cd manifests/metadata/base
-kustomize build . | kubectl apply -n kubeflow -f -
-```
+If you want to install the latest version of the Metadata component or to
+install the component as an application in your Kubernetes cluster, follow these 
+steps:
+
+1. Download the Kubeflow manifests repository:
 
-## Python Library
+    ```
+    git clone https://github.com/kubeflow/manifests
+    ```
 
-The Metadata project publishes a [Python library](https://github.com/kubeflow/metadata/tree/master/sdk/python#python-client) for logging metadata.
+2. Run the following commands to deploy the services of the Metadata component:
+
+    ```
+    cd manifests/metadata/base
+    kustomize build . | kubectl apply -n kubeflow -f -
+    ```
+
+## Using the Metadata SDK to record metadata
+
+The Metadata project publishes a 
+[Python library (SDK)](https://github.com/kubeflow/metadata/tree/master/sdk/python#python-client)
+that you can use to log (record) your metadata.
+
+Run the following command to install the Metadata SDK:
 
-You can install it via the following command:
 ```
 pip install kfmd
 ```
 
-To help you describe your ML workflows, the Python library has [predefined types](https://github.com/kubeflow/metadata/tree/master/schema) to capture models, datasets, evaluation metrics, and executions.
+<a id="demo-notebook"></a>
+### Try the Metadata SDK in a sample Jupyter notebook
+
+You can find an example of how to use the Metadata SDK in this 
+[`demo` notebook](https://github.com/kubeflow/metadata/blob/master/sdk/python/demo.ipynb).
+
+To run the notebook in your Kubeflow cluster:
+
+1. Follow the guide to 
+  [setting up your Jupyter notebooks in Kubeflow](/docs/notebooks/setup/).
+1. Go to the [`demo` notebook on 
+  GitHub](https://github.com/kubeflow/metadata/blob/master/sdk/python/demo.ipynb).
+1. Download the notebook code by opening the **Raw** view of the file, then 
+  right-clicking on the content and saving the file locally as `demo.ipynb`.
+1. Go back to your Jupyter notebook server in the Kubeflow UI. (If you've
+  moved away from the notebooks section in Kubeflow, click
+  **Notebook Servers** in the left-hand navigation panel to get back there.)
+1. In the Jupyter notebook UI, click **Upload** and follow the prompts to upload
+  the `demo.ipynb` notebook.
+1. Click the notebook name (`demo.ipynb`) to open the notebook in your Kubeflow
+  cluster.
+1. Run the steps in the notebook to install and use the Metadata SDK.
+
+When you have finished running through the steps in the `demo.ipynb` notebook,
+you can view the resulting metadata on the Kubeflow UI:
+
+1. Click **Artifact Store** in the left-hand navigation panel on the Kubeflow 
+  UI.
+1. On the **Artifacts** screen you should see the following items:
+
+  * A **model** metadata item with the name **MNIST**.
+  * A **metrics** metadata item with the name **MNIST-evaluation**.
+  * A **dataset** metadata item with the name **mytable-dump**.
+
+    You can click the name of each item to view the details. See the section
+    below about the [Metadata UI](#metadata-ui) for more details. 
+
+### Learn more about the Metadata SDK
+
+The Metadata SDK includes the following
+[predefined types](https://github.com/kubeflow/metadata/tree/master/schema)
+that you can use to describe your ML workflows:
+
+* [`data_set.json`](https://github.com/kubeflow/metadata/blob/master/schema/alpha/artifacts/data_set.json)
+  to capture metadata for a dataset that forms the input into or the output of
+  a component in your workflow.
+* [`execution.json`](https://github.com/kubeflow/metadata/blob/master/schema/alpha/execution.json)
+  to capture metadata for an execution (run) of your ML workflow.
+* [`metrics.json`](https://github.com/kubeflow/metadata/blob/master/schema/alpha/artifacts/metrics.json)
+  to capture metadata for the metrics used to evaluate an ML model.
+* [`model.json`](https://github.com/kubeflow/metadata/blob/master/schema/alpha/artifacts/model.json)
+  to capture metadata for an ML model that your workflow produces.
 
-You can find an example of how to use the logging API in this [notebook](https://github.com/kubeflow/metadata/blob/master/sdk/python/demo.ipynb).
+<a id="metadata-ui"></a>
+## Tracking artifacts on the Metadata UI
 
-## Backend
+You can view a list of logged artifacts and the details of each individual 
+artifact in the **Artifact Store** on the Kubeflow UI.
 
-The backend uses [ML-Metadata](https://github.com/google/ml-metadata/blob/master/g3doc/get_started.md) to manage all the metadata and relations. It exposes a [REST API](/docs/reference/metadata/v1alpha1/kubeflow-metadata-api-spec/).
+1. Go to Kubeflow in your browser. (If you haven't yet opened the 
+  Kubeflow UI, find out how to [access the
+  Kubeflow UIs](https://www.kubeflow.org/docs/other-guides/accessing-uis/).)
+1. Click **Artifact Store** in the left-hand navigation panel:
+  <img src="/docs/images/metadata-ui-option.png" 
+    alt="Metadata UI"
+    class="mt-3 mb-3 border border-info rounded">
 
-## UI
+1. The **Artifacts** screen opens and displays a list of items for all the
+  metadata events that your workflows have logged. You can click the name of 
+  each item to view the details. 
+
+    The following examples show the items that appear when you run the 
+    `demo.ipynb` notebook described [above](#demo-notebook):
+
+    <img src="/docs/images/metadata-artifacts-list.png" 
+    alt="A list of metadata items"
+    class="mt-3 mb-3 border border-info rounded">
+
+  * Example of **model** metadata with the name "MNIST":
+
+        <img src="/docs/images/metadata-model.png" 
+          alt="Model metadata for an example MNIST model"
+          class="mt-3 mb-3 border border-info rounded">
+
+  * Example of **metrics** metadata with the name "MNIST-evaluation":
+
+        <img src="/docs/images/metadata-metrics.png" 
+          alt="Metrics metadata for an evaluation of an MNIST model"
+          class="mt-3 mb-3 border border-info rounded">
+
+  * Example of **dataset** metadata with the name "mytable-dump":
+
+        <img src="/docs/images/metadata-dataset.png" 
+          alt="Dataset metadata"
+          class="mt-3 mb-3 border border-info rounded">
+
+
+
+## Backend and REST API
+
+The Kubeflow metadata backend uses [ML Metadata
+(MLMD)](https://github.com/google/ml-metadata/blob/master/g3doc/get_started.md) 
+to manage the metadata and relationships. 
+
+The backend exposes a 
+[REST API](/docs/reference/metadata/v1alpha1/kubeflow-metadata-api-spec/).
+
+You can add your own metadata types so that you can log metadata for custom
+artifacts. To add a custom type, send a REST API request to the
+[`artifact_types` endpoint](/docs/reference/metadata/v1alpha1/kubeflow-metadata-api-spec/#operation--api-v1alpha1-artifact_types-post). 
+
+For example, The following request registers an artifact type with 
+_name_ `myorg/mytype/v1` and three _properties_: 
+
+* `f1` (string)
+* `f2` (integer)
+* `f3` (double)
+
+```
+curl -X POST http://localhost:8080/api/v1alpha1/artifact_types \
+  --header "Content-Type: application/json" -d \
+  '{"name":"myorg/mytype/v1","properties":{"f1":"STRING", "f2":"INT", "f3": "DOUBLE"}}'
+```
 
-You can view a list of logged artifacts and the details of each individual artifact via the _Artifact Store_ on [Kubeflow UIs](https://www.kubeflow.org/docs/other-guides/accessing-uis/).
+## Next steps
 
+Run the 
+[xgboost-synthetic notebook](https://github.com/kubeflow/examples/tree/master/xgboost_synthetic)
+to build, train, and deploy an XGBoost model using Kubeflow Fairing and Kubeflow
+Pipelines with synthetic data. Examine the metadata output after running
+through the steps in the notebook.