Added an overview of Pipelines interfaces (#829)

* Added an overview of Pipelines interfaces.

* Updated for review comments.

content/docs/pipelines/overview/interfaces.md

++++
+title = "Introduction to the Pipelines Interfaces"
+description = "The ways you can interact with the Kubeflow Pipelines system"
+weight = 20
++++
+
+This page introduces the interfaces that you can use to build and run
+machine learning (ML) workflows with Kubeflow Pipelines.
+
+## User interface (UI)
+
+You can access the Kubeflow Pipelines UI by clicking **Pipeline Dashboard** on 
+the Kubeflow UI. The Kubeflow Pipelines UI looks like this:
+  <img src="/docs/images/pipelines-ui.png" 
+    alt="Pipelines UI"
+    class="mt-3 mb-3 border border-info rounded">
+
+From the Kubeflow Pipelines UI you can perform the following tasks:
+
+* Run one or more of the preloaded samples to try out pipelines quickly.
+* Upload a pipeline as a compressed file. The pipeline can be one that you
+  have built (see how to [build a 
+  pipeline](/docs/pipelines/sdk/build-component/#compile-the-pipeline)) or one 
+  that someone has shared with you.
+* Create an *experiment* to group one or more of your pipeline runs.
+  See the [definition of an
+  experiment](/docs/pipelines/overview/concepts/experiment/).
+* Create and start a *run* within the experiment. A run is a single execution
+  of a pipeline. See the [definition of a
+  run](/docs/pipelines/overview/concepts/run/).
+* Explore the configuration, graph, and output of your pipeline run.
+* Compare the results of one or more runs within an experiment.
+* Schedule runs by creating a recurring run.
+
+See the [quickstart guide](/docs/pipelines/pipelines-quickstart/) for more
+information about accessing the Kubeflow Pipelines UI and running the samples.
+
+When building a pipeline component, you can write out information for display
+in the UI. See the guides to [exporting 
+metrics](/docs/pipelines/sdk/pipelines-metrics/) and [visualizing results in 
+the UI](/docs/pipelines/sdk/output-viewer/).
+
+## Python SDK
+
+The Kubeflow Pipelines SDK provides a set of Python packages that you can use to 
+specify and run your ML workflows.
+
+See the [introduction to the Kubeflow Pipelines 
+SDK](/docs/pipelines/sdk/sdk-overview/) for an overview of the ways you can
+use the SDK to build pipeline components and pipelines.
+
+## REST API
+
+The Kubeflow Pipelines API is useful for continuous integration/deployment
+systems, for example, where you want to incorporate your pipeline executions
+into shell scripts or other systems. 
+For example, you may want to trigger a pipeline run when new data comes in.
+
+See the [Kubeflow Pipelines API reference 
+documentation](/docs/pipelines/reference/api/kubeflow-pipeline-api-spec/).