Pointed doc links to Kubeflow website instead of wiki.

README.md

@@ -15,7 +15,7 @@ The Kubeflow pipelines service has the following goals:
 
 ## Documentation
 
-Get started with your first pipeline and read further information in the [documentation](https://github.com/kubeflow/pipelines/wiki).
+Get started with your first pipeline and read further information in the [Kubeflow Pipelines documentation](https://www.kubeflow.org/docs/guides/pipelines/pipelines-overview/).
 
 ## Blog posts
 

components/README.md

@@ -1,9 +1,9 @@
 # Kubeflow pipeline components
 
 Kubeflow pipeline components are implementations of Kubeflow pipeline tasks. Each task takes
-one or more [artifacts](https://github.com/kubeflow/pipelines/wiki/Concepts#step-output-artifacts)
+one or more [artifacts](https://www.kubeflow.org/docs/guides/pipelines/pipelines-concepts/#step-output-artifacts)
 as input and may produce one or more
-[artifacts](https://github.com/kubeflow/pipelines/wiki/Concepts#step-output-artifacts) as output.
+[artifacts](https://www.kubeflow.org/docs/guides/pipelines/pipelines-concepts/#step-output-artifacts) as output.
 
 
 **Example: XGBoost DataProc components**
@@ -31,4 +31,4 @@ Note the naming convention for client code and runtime code—for a task nam
 * The `mytask.py` program contains the client code.
 * The `mytask` directory contains all the runtime code.
 
-See [how to build your own components](https://github.com/kubeflow/pipelines/wiki/Build-Your-Own-Component)
+See [how to build your own components](https://www.kubeflow.org/docs/guides/pipelines/build-component/)

samples/README.md

@@ -1,3 +1,3 @@
 The sample pipelines give you a quick start to building and deploying machine learning pipelines with Kubeflow.
-* Follow the guide to [deploy the Kubeflow pipelines service](https://github.com/kubeflow/pipelines/wiki/Deploy-the-Kubeflow-Pipelines-Service).
-* Build and deploy your pipeline [using the provided samples](https://github.com/kubeflow/pipelines/wiki/Samples).
+* Follow the guide to [deploy the Kubeflow pipelines service](https://www.kubeflow.org/docs/guides/pipelines/deploy-pipelines-service/).
+* Build and deploy your pipeline [using the provided samples](https://www.kubeflow.org/docs/guides/pipelines/pipelines-samples/).

samples/basic/README.md

@@ -4,7 +4,7 @@ This page tells you how to use the _basic_ sample pipelines contained in the rep
 
 ## Compile the pipeline specification
 
-Follow the guide to [building a pipeline](https://github.com/kubeflow/pipelines/wiki/Build-a-Pipeline) to install the Kubeflow Pipelines SDK and compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file. 
+Follow the guide to [building a pipeline](https://www.kubeflow.org/docs/guides/pipelines/build-pipeline/) to install the Kubeflow Pipelines SDK and compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file. 
 
 For convenience, you can download a pre-compiled, compressed YAML file containing the
 specification of the `sequential.py` pipeline. This saves you the steps required

samples/kubeflow-tf/README.md

@@ -14,7 +14,7 @@ Note: The trainer depends on Kubeflow API version v1alpha2.
 
 ## Compiling the pipeline template
 
-Follow the guide to [building a pipeline](https://github.com/kubeflow/pipelines/wiki/Build-a-Pipeline) to install the Kubeflow Pipelines SDK, then run the following command to compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file.
+Follow the guide to [building a pipeline](https://www.kubeflow.org/docs/guides/pipelines/build-pipeline/) to install the Kubeflow Pipelines SDK, then run the following command to compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file.
 
 ```bash
 dsl-compile --py kubeflow-training-classification.py --output kubeflow-training-classification.tar.gz

samples/tfx/README.md

@@ -29,7 +29,7 @@ Therefore, you must enable the DataFlow API for the given GCP project if you wan
 
 ## Compiling the pipeline template
 
-Follow the guide to [building a pipeline](https://github.com/kubeflow/pipelines/wiki/Build-a-Pipeline) to install the Kubeflow Pipelines SDK, then run the following command to compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file.
+Follow the guide to [building a pipeline](https://www.kubeflow.org/docs/guides/pipelines/build-pipeline/) to install the Kubeflow Pipelines SDK, then run the following command to compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file.
 
 ```bash
 dsl-compile --py taxi-cab-classification-pipeline.py --output taxi-cab-classification-pipeline.tar.gz

samples/xgboost-spark/README.md

@@ -14,7 +14,7 @@ Preprocessing uses Google Cloud DataProc. Therefore, you must enable the [DataPr
 
 ## Compile
 
-Follow the guide to [building a pipeline](https://github.com/kubeflow/pipelines/wiki/Build-a-Pipeline) to install the Kubeflow Pipelines SDK and compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file. 
+Follow the guide to [building a pipeline](https://www.kubeflow.org/docs/guides/pipelines/build-pipeline/) to install the Kubeflow Pipelines SDK and compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file. 
 
 ## Deploy