Linked to the new Pipelines SDK intro from various pages (#827)

* Linked to Pipelines SDK intro from various pages.

* Tweaks.

* Update content/docs/pipelines/tutorials/build-pipeline.md
Co-Authored-By: NDwight J Lyle <dwightjl@users.noreply.github.com>

content/docs/notebooks/setup.md

@@ -256,6 +256,8 @@ exposed to the internet and is an unsecured endpoint by default.
 
 ## Next steps
 
+* Build machine-learning pipelines with the [Kubeflow Pipelines 
+  SDK](/docs/pipelines/sdk/sdk-overview/).
 * Explore [Kubeflow Fairing](/docs/fairing/) for a complete solution to 
   building, training, and deploying an ML model from a notebook.
 * Learn the advanced features available from a Kubeflow notebook, such as

content/docs/pipelines/overview/pipelines-overview.md

@@ -10,7 +10,7 @@ scalable machine learning (ML) workflows based on Docker containers.
 ## Quickstart
 
 Run your first pipeline by following the 
-[pipelines quickstart guide](/docs/guides/pipelines/pipelines-quickstart).
+[pipelines quickstart guide](/docs/pipelines/pipelines-quickstart).
 
 ## What is Kubeflow Pipelines?
 
@@ -213,9 +213,11 @@ At a high level, the execution of a pipeline proceeds as follows:
 ## Next steps
 
 * Follow the 
-  [pipelines quickstart guide](/docs/guides/pipelines/pipelines-quickstart) to 
+  [pipelines quickstart guide](/docs/pipelines/pipelines-quickstart) to 
   deploy Kubeflow and run a sample pipeline directly from the 
   Kubeflow Pipelines UI.
+* Build machine-learning pipelines with the [Kubeflow Pipelines 
+  SDK](/docs/pipelines/sdk/sdk-overview/).
 * Follow the full guide to experimenting with
   [the Kubeflow Pipelines samples](/docs/pipelines/tutorials/build-pipeline/).
   

content/docs/pipelines/pipelines-quickstart.md

@@ -187,3 +187,5 @@ finished with them:
   Pipelines UI. Next, you may want to run a pipeline from a notebook, or compile 
   and run a sample from the code. See the guide to experimenting with
   [the Kubeflow Pipelines samples](/docs/pipelines/tutorials/build-pipeline/).
+* Build your own machine-learning pipelines with the [Kubeflow Pipelines 
+  SDK](/docs/pipelines/sdk/sdk-overview/).

content/docs/pipelines/sdk/install-sdk.md

@@ -92,7 +92,7 @@ The response should be something like this:
 
 ## Next steps
 
+* [See how to use the SDK](/docs/pipelines/sdk/sdk-overview/).
 * [Build a component and a pipeline](/docs/pipelines/sdk/build-component/).
-* [Get started](/docs/pipelines/pipelines-quickstart) with the 
-  Kubeflow Pipelines UI.
-* Read more about [pipeline concepts](/docs/pipelines/concepts/).
+* [Get started with the UI](/docs/pipelines/pipelines-quickstart).
+* [Understand pipeline concepts](/docs/pipelines/concepts/).

content/docs/pipelines/tutorials/build-pipeline.md

@@ -125,6 +125,8 @@ The following notebooks are available:
 
 ## Next steps
 
+* Learn the various ways to use the [Kubeflow Pipelines 
+  SDK](/docs/pipelines/sdk/sdk-overview/).
 * See how to 
   [build your own pipeline components](/docs/pipelines/sdk/build-component/).
 * Read more about 

content/docs/pipelines/tutorials/pipelines-tutorial.md

@@ -565,6 +565,11 @@ gsutil rm -r gs://${BUCKET_NAME}
 As an alternative to the command line, you can delete the various resources 
 using the [GCP Console][gcp-console].
 
+## Next steps
+
+Build your own machine-learning pipelines with the [Kubeflow Pipelines 
+SDK](/docs/pipelines/sdk/sdk-overview/).
+
 [mnist-data]: http://yann.lecun.com/exdb/mnist/index.html
 
 [tensorflow]: https://www.tensorflow.org/

content/docs/started/getting-started.md

@@ -67,10 +67,12 @@ to suit your environment (cloud, on premises (on prem), or local):
 
 See the [Kubeflow troubleshooting guide](/docs/other-guides/troubleshooting/).
 
-## Resources
+## Next steps
 
-* The [documentation](/docs/) provides in-depth instructions for using Kubeflow.
-* Self-paced scenarios for learning and trying out Kubeflow:
+* See the [documentation](/docs/) for in-depth instructions on using Kubeflow.
+* Build machine-learning pipelines with the [Kubeflow Pipelines 
+  SDK](/docs/pipelines/sdk/sdk-overview/).
+* Explore the self-paced scenarios for learning and trying out Kubeflow:
   * [Codelabs](https://codelabs.developers.google.com/?cat=tensorflow)
       * [Introduction to Kubeflow on Google Kubernetes
         Engine](https://codelabs.developers.google.com/codelabs/kubeflow-introduction/index.html)