diff --git a/content/docs/about/events.md b/content/docs/about/events.md
index 7885726e4e..580a47c2f4 100644
--- a/content/docs/about/events.md
+++ b/content/docs/about/events.md
@@ -16,6 +16,11 @@ Please raise a [GitHub issue](https://github.com/kubeflow/website/issues/new) if
 * [Data Day Texas, Austin](https://datadaytexas.com/), 26 January, 2019
   - Kubeflow: Portable Machine Learning on Kubernetes: Michelle Casbon
 * [KubeCon, Seattle](https://events.linuxfoundation.org/events/kubecon-cloudnativecon-north-america-2018/), 11-13 December, 2018
+  - [Workshop: Kubeflow End-to-End: GitHub Issue Summarization](https://sched.co/GrWE): Amy Unruh, Michelle Casbon
+  - [Natural Language Code Search for GitHub Using Kubeflow](https://sched.co/GrVn): Jeremy Lewi, Hamel Husain
+  - [Eco-Friendly ML: How the Kubeflow Ecosystem Bootstrapped Itself](https://sched.co/GrTc): Peter McKinnon
+  - [Deep Dive: Kubeflow BoF](https://sched.co/Ha1X): Jeremy Lewi, David Aronchick
+  - [Machine Learning as Code](https://sched.co/GrVh): Jay Smith
 * Women in ML & Data Science, Melbourne, 5 December, 2018
   - Panel: Juliet Hougland, Michelle Casbon
 * [YOW!, Melbourne](https://melbourne.yowconference.com.au/), 4-7 December, 2018
@@ -33,7 +38,8 @@ Please raise a [GitHub issue](https://github.com/kubeflow/website/issues/new) if
   - Kubeflow End to End: Amy Unruh
 * [Data@Scale, Boston](https://dataatscale2018.splashthat.com/), 25 October, 2018
   - [Women in Engineering Panel](https://datascalewomensbreakfast.splashthat.com/): Michelle Casbon
-  - Kubeflow: Portable Machine Learning on Kubernetes: Michelle Casbon
+  - [Kubeflow: Portable Machine Learning on Kubernetes](https://code.fb.com/core-data/data-scale-boston/): Michelle Casbon
+      - [Video](https://www.facebook.com/atscaleevents/videos/114311602829170/)
 * [Kafka Summit, San Francisco](https://kafka-summit.org/), 16-17 October, 2018
 * [O’Reilly AI Conference, London](https://conferences.oreilly.com/artificial-intelligence/ai-eu), 08-11 October, 2018
   - [Machine Learning at Scale with Kubernetes](https://conferences.oreilly.com/artificial-intelligence/ai-eu/public/schedule/detail/69194): Chris Cho
diff --git a/content/docs/guides/components/hyperparameter.md b/content/docs/guides/components/hyperparameter.md
index 92e291cf3e..58171d433d 100644
--- a/content/docs/guides/components/hyperparameter.md
+++ b/content/docs/guides/components/hyperparameter.md
@@ -9,30 +9,17 @@ toc = true
   weight = 5
 +++
 
-## Deploying Katib
-
-[Katib](https://github.com/kubeflow/katib) is a hyperparameter tuning framework, inspired by
-[Google Vizier](https://static.googleusercontent.com/media/research.google.com/ja//pubs/archive/bcb15507f4b52991a0783013df4222240e942381.pdf).
-
-To deploy katib,
-```shell
-ks pkg install kubeflow/katib@master
-ks generate katib katib
-ks apply ${ENV} -c katib
-```
-
 ## Using Katib
 
-Create namespace `katib` as the service launches jobs in this namespace.
+Currently we are using port-forwarding to access the katib services.
+kubernetes version 1.9~
 ```
-kubectl create namespace katib
+kubectl -n kubeflow port-forward svc/katib-ui 8000:80
 ```
-
-Currently we are using port-forwarding to access the katib services.
+~1.8
 ```
-kubectl get pod -n kubeflow  # Find your vizier-core and modedb-frontend pods
-kubectl port-forward -n kubeflow [vizier-core pod] 6789:6789 &
-kubectl port-forward -n kubeflow [modeldb-frontend pod] 3000:3000 &
+kubectl get pod -n kubeflow  # Find your katib-ui pods
+kubectl port-forward -n kubeflow [katib-ui pod] 8000:80 &
 ```
 ## Creating a Study Job
 You can create Study Job for Katib by defining a StudyJob config file.
@@ -55,15 +42,15 @@ In this demo, 3 hyper parameters
 are randomly generated.
 
 ```
-$ kubectl -n katib get studyjob
+$ kubectl -n kubeflow get studyjob
 ```
 
 Check the study status.
 
 ```
-$ kubectl -n katib describe studyjobs random-example
+$ kubectl -n kubeflow describe studyjobs random-example
 Name:         random-example
-Namespace:    katib
+Namespace:    kubeflow
 Labels:       controller-tools.k8s.io=1.0
 Annotations:  kubectl.kubernetes.io/last-applied-configuration={"apiVersion":"kubeflow.org/v1alpha1","kind":"StudyJob","metadata":{"annotations":{},"labels":{"controller-tools.k8s.io":"1.0"},"name":"random-example"...
 API Version:  kubeflow.org/v1alpha1
@@ -73,7 +60,7 @@ Metadata:
   Creation Timestamp:  2018-08-15T01:29:13Z
   Generation:          0
   Resource Version:    173289
-  Self Link:           /apis/kubeflow.org/v1alpha1/namespaces/katib/studyjobs/random-example
+  Self Link:           /apis/kubeflow.org/v1alpha1/namespaces/kubeflow/studyjobs/random-example
   UID:                 9e136400-a02a-11e8-b88c-42010af0008b
 Spec:
   Study Spec:
@@ -140,4 +127,4 @@ Events:                <none>
 
 It should start a study and run two jobs with different parameters.
 
-Go to http://localhost:3000/katib to see the result.
+Go to http://localhost:8000/katib to see the result.
diff --git a/content/docs/guides/components/tftraining.md b/content/docs/guides/components/tftraining.md
index 783f86a964..9635f77acf 100644
--- a/content/docs/guides/components/tftraining.md
+++ b/content/docs/guides/components/tftraining.md
@@ -1,5 +1,6 @@
 +++
-title = "TensorFlow Training"
+title = "TensorFlow Training (TFJob)"
+linkTitle = "TensorFlow Training"
 description = ""
 weight = 10
 toc = true
@@ -147,7 +148,7 @@ the [`TFJob` custom resource](https://github.com/kubeflow/tf-operator) is availa
 
 We treat each TensorFlow job as a [component](https://ksonnet.io/docs/tutorial#2-generate-and-deploy-an-app-component) in your APP.
 
-### Run the TfCnn example
+### Running the TfCnn example
 
 Kubeflow ships with a [ksonnet prototype](https://ksonnet.io/docs/concepts#prototype) suitable for running the [TensorFlow CNN Benchmarks](https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks).
 
@@ -203,8 +204,33 @@ Typically you will want to change the following values
 
    * For example, you might need to configure various environment variables to talk to datastores like GCS or S3
 
-1. Attach PV's if you want to use PVs for storage.
+1. Attach PVs if you want to use PVs for storage.
 
+### Accessing the TFJob dashboard
+
+The TFJob dashboard is available at `<path>/tfjobs/ui/`. Specifically:
+
+* If you're using the central Kubeflow UI, you can access the TFJob dashboard
+  by clicking **TFJOB DASHBOARD**:
+
+    ![Central UI](/docs/images/central-ui.png)
+
+* If you followed the
+ [guide for GKE](/docs/started/getting-started-gke), you can
+ access the TFJob dashboard at the following URL:
+
+    ```
+    https://<deployment-name>.endpoints.<project>.cloud.goog/tfjobs/ui/
+    ```
+
+* If you're using portforwarding, you can access the TFJob dashboard at the
+  following URL:
+
+    ```
+    http://localhost:8080/tfjobs/ui/
+    ```
+
+See more details about [accessing the Kubeflow UIs](/docs/guides/accessing-uis).
 
 ## Using GPUs
 
diff --git a/content/docs/started/getting-started-gke.md b/content/docs/started/getting-started-gke.md
index e6926000db..ba3519f5c6 100644
--- a/content/docs/started/getting-started-gke.md
+++ b/content/docs/started/getting-started-gke.md
@@ -73,7 +73,14 @@ Create an OAuth client ID to be used to identify Cloud IAP when requesting acces
     export CLIENT_SECRET=<CLIENT_SECRET from OAuth page>
     ```
 
-## Deploy Kubeflow on Kubernetes Engine
+## Deploy Kubeflow on GKE using the UI
+
+1. Open [https://deploy.kubeflow.cloud/](https://deploy.kubeflow.cloud/#/deploy) in your web browser.
+1. Sign in using a GCP account with admin privileges for your GCP project.
+1. Complete the form.
+1. Click **Create Deployment**.
+
+## Deploy Kubeflow on GKE using the command line
 
 Run the following steps to deploy Kubeflow:
 
diff --git a/themes/kf/layouts/index.html b/themes/kf/layouts/index.html
index 05f9409756..bf8dd83dfa 100644
--- a/themes/kf/layouts/index.html
+++ b/themes/kf/layouts/index.html
@@ -42,7 +42,7 @@ <h4>Notebooks</h4>
 				</div>
 				<div class="text">
 					<h4>TensorFlow model training</h4>
-					<p>A TensorFlow Training Controller that can be configured to use either CPU’s or GPUs and be dynamically adjusted to the size of a cluster with a single setting. We also provide a TensorFlow job operator. </p>
+					<p>A TensorFlow Training Controller that can be configured to use either CPUs or GPUs and be dynamically adjusted to the size of a cluster with a single setting. We also provide a TensorFlow job operator. </p>
 				</div>
 			</div>
 
diff --git a/themes/kf/sass/src/index.html b/themes/kf/sass/src/index.html
index fdacd80c86..6d9f2d400c 100755
--- a/themes/kf/sass/src/index.html
+++ b/themes/kf/sass/src/index.html
@@ -149,7 +149,7 @@ <h4>Notebooks</h4>
 				</div>
 				<div class="text">
 					<h4>TensorFlow model training</h4>
-					<p>A TensorFlow Training Controller that can be configured to use either CPU’s or GPUs and be dynamically adjusted to the size of a cluster with a single setting. We also provide a TensorFlow job operator. </p>
+					<p>A TensorFlow Training Controller that can be configured to use either CPUs or GPUs and be dynamically adjusted to the size of a cluster with a single setting. We also provide a TensorFlow job operator. </p>
 				</div>
 			</div>