diff --git a/data/quickstarts/deploy-watson-model-quickstart.yaml b/data/quickstarts/deploy-watson-model-quickstart.yaml
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+kind: ConsoleQuickStart
+metadata:
+  name: build-deploy-watson-model
+spec:
+  displayName: Deploying a Model with Watson Studio
+  durationMinutes: 15
+  icon: 'images/ibm.svg'
+  description: This quick start will walk you through importing a Notebook in Watson Studio, deploying a model, and monitoring with Open Scale.
+  introduction: |-
+    ### This quick start will walk you through importing a Notebook in Watson Studio, deploying a model, and monitoring with Open Scale.
+    Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, 
+    the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture.
+
+  tasks:
+    - title: Create a Project
+      description: |-
+        ### Create a Project
+        1. Choose Projects > View all projects from the menu and then click New project on the My Projects page.
+        2. Select Analytics project and click OK.
+        4. Click Create. 
+      summary:
+        success: You have launched created a new project
+        failed: Try the steps again
+    - title: Accessing Data Locally
+      description: |-
+        ### After you create a project, you add data assets to it so that you can work with data
+        1. Add a data file to your project from your local system
+        2. From your project’s Assets page, click Add to project > Data. 
+        3. In the Load pane that opens, browse for files or drag them onto the pane. 
+      summary:    
+        success: The files are listed as data assets on the Assets page of your project.   
+        failed: Try the steps again   
+    - title: Import a notebook into your project
+      description: |-
+        ### After you have data assets you are ready to import a notebook
+        1. From your project, click Add to Project > Notebook.
+        2. On the New Notebook page, upload a notebook file from your file system, or a URL. 
+        3. Specify the runtime environment for the language you want to use (Python, R, or Scala). 
+        4. Click Create Notebook. 
+      summary:    
+        success: The notebook opens in the Jupyter notebook editor.  
+        failed: Try the steps again
+     - title: Load data into your notebook
+      description: |-
+        ### After you have a notebook created you can load data into the notebook
+        1. Click in an empty code cell in your notebook.
+        2. Click the Find and Add Data icon.
+        3. Click Insert to code > pandas DataFrame right below the data file name.
+        4. Run the cell.
+      summary:    
+        success: The data is now availble to load from the notebook.  
+        failed: Try the steps again
+      - title: Training an AutoAI model
+      description: |-
+        ### After you have a notebook with data loaded, you can start building a model
+        1. From the Assets page of your project, click Add to Project >AutoAI experiment.
+        2. Name your experiment, then click Create.
+        3. Upload or add from project the CSV file you will use to train the experiment.
+        4. Choose the prediction column.
+        5. Run the experiment.
+      summary:    
+        success: You have trained a model.  
+        failed: Try the steps again
+      - title: Save and Deploy a model
+      description: |-
+        ### After a model is trained, it can then be deployed 
+        1. After the AutoAI experiment finishes training, choose the best performing pipeline and click Save as model.
+        2. A notification indicates the model is saved. Click the View in project link in the notification to open the model details page.
+        3. Create a deployment space, and then promote the model to the deployment space.
+        4. Click the link in the success notification to open the model in the deployment space.
+        5. Create and name a new deployment of the model
+        6. When the deployment is ready, click the deployment name and choose Online as the deployment type, assigning a name for the deployment.
+        7. When the deployment is ready, click the name to view and test the deployment.
+        8. Click the Test tab and use the form interface to enter test values.
+        9. Click Predict to view the prediction.
+      summary:    
+        success: You have deployed an AutoAI model.  
+        failed: Try the steps again  
+  conclusion: You are now able to import a notebook in Watson Studio, build, and deploy a model.
+  nextQuickStart: []
diff --git a/data/quickstarts/seldon-depploy-canaray-quickstart.yaml b/data/quickstarts/seldon-depploy-canaray-quickstart.yaml
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+kind: ConsoleQuickStart
+metadata:
+  name: seldon-deploy-model-canary
+spec:
+  displayName: Launch a SKLearn model and update model by canarying
+  durationMinutes: 10
+  icon: 'images/seldon.svg'
+  description: How to perform a canary promotion of a Scikit-Learn model
+  prerequisites: [You completed the "Create a Jupyter notebook" quick start.]
+  introduction: |-
+    ### This quick start shows you how to launch a SKLearn model and update model by canarying.
+    Seldon Deploy is a specialist set of tools designed to simplify and accelerate the process of deploying and managing your machine learning models.
+  tasks:
+    - title: Deploy a pretrained sklearn iris model
+      description: |-
+        ### Deploy a pretrained sklearn iris model
+        1. Open Seldon Console and click create. Deployment creation wizard appears.
+        2. Put the location of the model url. For example: gs://seldon-models/sklearn/iris 
+      summary:
+        success: You have deployed a pretrained sklearn iris model
+        failed: Try the steps again.
+    - title: Start Load Test
+      description: |-
+        ### Complete the load test wizard:
+        1. Use the request.json file in this folder:
+        2 {"data": {"names": ["Sepal length","Sepal width","Petal length", "Petal Width"], "ndarray": [[6.8,  2.8,  4.8,  1.4], [6.0,  3.4,  4.5,  1.6]]}}
+        3. When running you should see metrics on dashboard. Enter the request logs screen to view request payloads.
+      summary:
+        success: You successfully performed a load test against the pre-trained iris model.
+        failed: Try the steps again.
+    - title: Create Canary
+      description: |-
+        ### Create and promote an XGBoost canary model
+        1. Create an XGBoost canary model using the saved model at: gs://seldon-models/xgboost/iris
+        2. Rerun the load test and you should see metrics for both default and canary models.
+        3. Promote the XGBoost Canary to be the main model.
+      summary:
+        success: You have successfully promoted the XGBoost to be the canary model.
+        failed: Try the steps again.
+  conclusion: You are now able to promote a model.
+  nextQuickStart: []