diff --git a/docs/en/2-Publishing/Dash.md b/docs/en/2-Publishing/Dash.md
index d7bf57c46..73befdbba 100644
--- a/docs/en/2-Publishing/Dash.md
+++ b/docs/en/2-Publishing/Dash.md
@@ -25,7 +25,7 @@ Dash makes it simple to build an interactive GUI around your data analysis code.
This is an example of a Layout With Figure and Slider from
[Dash](https://dash.plotly.com/basic-callbacks).
-
+
### Plotly Dash
diff --git a/docs/en/2-Publishing/Datasette.md b/docs/en/2-Publishing/Datasette.md
index dd62e4d7b..56724c164 100644
--- a/docs/en/2-Publishing/Datasette.md
+++ b/docs/en/2-Publishing/Datasette.md
@@ -36,7 +36,10 @@ You can even explore maps within the tool!
## Installing Datasette
In your Jupyter Notebook, open a terminal window and run the command
-`pip3 install datasette`. 
+`pip3 install datasette`.
+
+ 
+
## Starting Datasette
diff --git a/docs/en/2-Publishing/R-Shiny.md b/docs/en/2-Publishing/R-Shiny.md
index 126e41f18..52c1382b6 100644
--- a/docs/en/2-Publishing/R-Shiny.md
+++ b/docs/en/2-Publishing/R-Shiny.md
@@ -75,27 +75,27 @@ shinyuieditor::launch_editor(app_loc = "./")
The first thing you'll see is the template chooser. There are three options as of this writing (`shinyuieditor` is currently in alpha).
-
+
### Single or Multi File Mode
I recommend **Multi file mode**, this will put the back-end code in a file called `server.R` and front-end in a file called `ui.R`.
-
+
### Design Your App
You can design your app with either code or the graphical user interface. Try designing the layout with the GUI and designing the plots with code.
-
+
Any changes you make in `shinyuieditor` will appear immediately in the code.
-
+
Any change you make in the code will immediately appear in the `shinyuieditor`.
-
+
## Publishing on the AAW
diff --git a/docs/en/3-Pipelines/Argo.md b/docs/en/3-Pipelines/Argo.md
index 06d92e60a..45ab86835 100644
--- a/docs/en/3-Pipelines/Argo.md
+++ b/docs/en/3-Pipelines/Argo.md
@@ -1,7 +1,7 @@
## Argo Workflows
-
+
**[Argo Workflows](https://argoproj.github.io/argo-workflows/)** is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. Argo Workflows is implemented as a Kubernetes CRD (Custom Resource Definition). It is particularly well-suited for use in data science workflows and machine learning workflows.
@@ -19,7 +19,7 @@ With Argo Workflows, you can easily build workflows that incorporate tasks such
!!! info ""
- [](https://argoproj.github.io/argo-workflows/)
+ [](https://argoproj.github.io/argo-workflows/)
Argo Workflows
diff --git a/docs/en/4-Collaboration/Overview.md b/docs/en/4-Collaboration/Overview.md
index bf3f628e5..f06633aba 100644
--- a/docs/en/4-Collaboration/Overview.md
+++ b/docs/en/4-Collaboration/Overview.md
@@ -80,7 +80,7 @@ others are. That said, it is totally possible.
You can add or remove people from a namespace you already own through the
**Manage Contributors** menu in Kubeflow.
-
+
!!! info "Now you and your colleagues can share access to a server!"
diff --git a/docs/en/6-Gitlab/Gitlab.md b/docs/en/6-Gitlab/Gitlab.md
index 50f941818..633534980 100644
--- a/docs/en/6-Gitlab/Gitlab.md
+++ b/docs/en/6-Gitlab/Gitlab.md
@@ -10,10 +10,10 @@ Thankfully, using the cloud main GitLab on the AAW is just like how you would re
### Step 1: Locate the Git repo you want to clone and copy the clone with HTTPS option
If your repository is private, you will need to also do Step 4 (Creating a Personal Access Token) for this to go through.
For me this was a test repo
-
+
### Step 2: Paste the copied link into one of your workspace servers
-
+
### Step 3: Success!
As seen in the above screenshot I have cloned the repo!
@@ -22,7 +22,7 @@ As seen in the above screenshot I have cloned the repo!
If you try to `git push ....` you will encounter an error eventually leading you to the [GitLab help documentation](https://gitlab.k8s.cloud.statcan.ca/help/user/profile/account/two_factor_authentication.md#error-http-basic-access-denied-the-provided-password-or-token-)
You will need to make a Personal Access Token for this. To achieve this go in GitLab, click your profile icon and then hit `Preferences` and then `Access Tokens`
-
+
Follow the prompts entering the name, the token expiration date and granting the token permissions (I granted `write_repository`)
### Step 5: Personalize `Git` to be you
@@ -30,12 +30,12 @@ Run `git config user.email ....` and `git config user.name ...` to match your Gi
### Step 6: Supply the Generated Token when asked for your password
The token will by copy-able at the top once you hit `Create personal access token` at the bottom
-
+
Once you have prepared everything it's time
-
+
### Step 7: See the results of your hard work in GitLab
-
+
diff --git a/docs/en/7-MLOps/Machine-Learning-Model-Cloud-Storage.md b/docs/en/7-MLOps/Machine-Learning-Model-Cloud-Storage.md
index 0f5e821f6..cd1dd41fc 100644
--- a/docs/en/7-MLOps/Machine-Learning-Model-Cloud-Storage.md
+++ b/docs/en/7-MLOps/Machine-Learning-Model-Cloud-Storage.md
@@ -34,9 +34,9 @@ The AAW platform provides several types of storage:
Depending on your use case, either disk or bucket may be most suitable. Our [storage overview](../5-Storage/Overview.md) will help you compare them.
### Disks
-
-[](../5-Storage/Disks.md)
-
+
+ [](../5-Storage/Disks.md)
+
**[Disks](../5-Storage/Disks.md)** are added to your notebook server by adding Data Volumes.
### Data Lakes (Coming Soon)
diff --git a/docs/en/7-MLOps/Machine-Learning-Training-Pipelines.md b/docs/en/7-MLOps/Machine-Learning-Training-Pipelines.md
index 150e9a0b9..8d2fa5817 100644
--- a/docs/en/7-MLOps/Machine-Learning-Training-Pipelines.md
+++ b/docs/en/7-MLOps/Machine-Learning-Training-Pipelines.md
@@ -1,7 +1,7 @@
# Training Machine Learning Models on the AAW
-
+
@@ -394,7 +394,7 @@ Finally, you can deploy the trained machine learning model in a production envir
### Using Argo Workflows
-
+
!!! info "MLOps Best Practices"
diff --git a/docs/en/7-MLOps/PaaS-Integration.md b/docs/en/7-MLOps/PaaS-Integration.md
index 36f25b423..5ce7f47f9 100644
--- a/docs/en/7-MLOps/PaaS-Integration.md
+++ b/docs/en/7-MLOps/PaaS-Integration.md
@@ -19,7 +19,9 @@ to help!
_Integration is key to success._
+
[]()
+
Our open source platform offers unparalleled optionality to our users. By allowing users to use open source tools, we empower them to use their preferred data science and machine learning frameworks. But the real power of our platform comes from its ability to integrate with many Platform as a Service (PaaS) offerings, like Databricks or AzureML. This means that our users can leverage the power of the cloud to run complex data processing and machine learning pipelines at scale. With the ability to integrate with PaaS offerings, our platform enables our users to take their work to the next level, by giving them the power to scale their workloads with ease, and take advantage of the latest innovations in the field of data science and machine learning. By providing this level of optionality, we ensure that our users can always choose the right tool for the job, and stay ahead of the curve in an ever-changing field.
diff --git a/docs/en/index.md b/docs/en/index.md
index dab50f0a7..96236e6f4 100644
--- a/docs/en/index.md
+++ b/docs/en/index.md
@@ -25,7 +25,7 @@ No matter what stage of your data science journey you're at, the Advanced Analyt
## Getting Started with the AAW
-
+
### The AAW Portal