Merge pull request #1868 from StatCan/fix-alt-images

Fix alts and css

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).
 
-![dash_plot](../images/plot.png)
+![Dash Plot example](../images/plot.png)
 
 ### Plotly Dash
 

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`. ![Install Datasette](../images/InstallDatasette.PNG)
+`pip3 install datasette`. 
+<center>
+    ![Install Datasette](../images/InstallDatasette.PNG)
+</center>
 
 ## Starting Datasette
 

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).
 
-![image](https://user-images.githubusercontent.com/8212170/229583104-9404ad01-26cd-4260-bce6-6fe32ffab7d8.png)
+![Shiny ui Editor Template](https://user-images.githubusercontent.com/8212170/229583104-9404ad01-26cd-4260-bce6-6fe32ffab7d8.png)
 
 ### 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`.
 
-![image](https://user-images.githubusercontent.com/8212170/229584803-452bcdb9-4aa6-4902-805e-845d0b939016.png)
+![Generate app multi file mode](https://user-images.githubusercontent.com/8212170/229584803-452bcdb9-4aa6-4902-805e-845d0b939016.png)
 
 ### 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.
 
-![image](https://user-images.githubusercontent.com/8212170/229589867-19bf334c-4789-4228-99ec-44583b119e29.png)
+![App design example](https://user-images.githubusercontent.com/8212170/229589867-19bf334c-4789-4228-99ec-44583b119e29.png)
 
 Any changes you make in `shinyuieditor` will appear immediately in the code. 
 
-![image](https://user-images.githubusercontent.com/8212170/229637808-38dc0ed3-902a-44db-bfa0-193ef25af6ca.png)
+![Panel text example](https://user-images.githubusercontent.com/8212170/229637808-38dc0ed3-902a-44db-bfa0-193ef25af6ca.png)
 
 Any change you make in the code will immediately appear in the `shinyuieditor`.
 
-![image](https://user-images.githubusercontent.com/8212170/229637972-b4a263f5-27f0-4160-8b43-9250ace72999.png)
+![ShinyUiEditor](https://user-images.githubusercontent.com/8212170/229637972-b4a263f5-27f0-4160-8b43-9250ace72999.png)
 
 ## Publishing on the AAW
 

docs/en/3-Pipelines/Argo.md

@@ -1,7 +1,7 @@
 
 ## Argo Workflows
 
-![Argo Workflows](../images/argo.png)
+![Argo Workflows Squid Logo](../images/argo.png)
 
 **[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
 <!-- prettier-ignore -->
 !!! info ""
     <center>
-    [![Argo Workflows](../images/argo-workflows.jpg)](https://argoproj.github.io/argo-workflows/)
+    [![Argo Workflows Diagram](../images/argo-workflows.jpg)](https://argoproj.github.io/argo-workflows/)
     <h3>Argo Workflows</h3>
     </center>
 

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.
 
-![Contributors Menu](../images/kubeflow_contributors.png)
+![ Manage Contributors Menu](../images/kubeflow_contributors.png)
 
 <!-- prettier-ignore -->
 !!! info "Now you and your colleagues can share access to a server!"

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 
-![image](https://user-images.githubusercontent.com/23174198/217060353-ba229ced-b5c1-4eae-8878-9608835cc65f.png)
+![Clone with SSH image](https://user-images.githubusercontent.com/23174198/217060353-ba229ced-b5c1-4eae-8878-9608835cc65f.png)
 
 ### Step 2: Paste the copied link into one of your workspace servers
-![image](https://user-images.githubusercontent.com/23174198/217060697-535df6c1-d9bb-4bc3-a42b-9f085a5386d5.png)
+![Git clone example](https://user-images.githubusercontent.com/23174198/217060697-535df6c1-d9bb-4bc3-a42b-9f085a5386d5.png)
 
 ### Step 3: Success! 
 As seen in the above screenshot I have cloned the repo!
@@ -22,20 +22,20 @@ 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`
-![image](https://user-images.githubusercontent.com/23174198/217061060-122dded8-dc80-46ce-a907-a85913cf5dd7.png)
+![Personal Access Tokens](https://user-images.githubusercontent.com/23174198/217061060-122dded8-dc80-46ce-a907-a85913cf5dd7.png)
 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
 Run `git config user.email ....` and `git config user.name ...` to match your GitLab identity.
 
 ### 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
-![image](https://user-images.githubusercontent.com/23174198/217062846-03a715f1-ded5-4d80-ad4b-c647ae5e30fd.png)
+![Supply Personal Access Token](https://user-images.githubusercontent.com/23174198/217062846-03a715f1-ded5-4d80-ad4b-c647ae5e30fd.png)
 
 Once you have prepared everything it's time
-![image](https://user-images.githubusercontent.com/23174198/217063198-c1bd6c3a-ebc5-444d-98ba-24ef32faa20e.png)
+![Final steps](https://user-images.githubusercontent.com/23174198/217063198-c1bd6c3a-ebc5-444d-98ba-24ef32faa20e.png)
 
 
 ### Step 7: See the results of your hard work in GitLab
-![image](https://user-images.githubusercontent.com/23174198/217063990-efaa8e81-a0eb-4b6d-842e-2ca3112bb4f7.png)
+![GitLab menu](https://user-images.githubusercontent.com/23174198/217063990-efaa8e81-a0eb-4b6d-842e-2ca3112bb4f7.png)
 

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
-
-[![Disks](../images/Disks.PNG)](../5-Storage/Disks.md)
-
+<center>
+    [![Disks](../images/Disks.PNG)](../5-Storage/Disks.md)
+</center>
 **[Disks](../5-Storage/Disks.md)** are added to your notebook server by adding Data Volumes.
 
 ### Data Lakes (Coming Soon)

docs/en/7-MLOps/Machine-Learning-Training-Pipelines.md

@@ -1,7 +1,7 @@
 # Training Machine Learning Models on the AAW
 
 <center>
-![MLOps](../images/mlops.jpg)
+![Robots in work](../images/mlops.jpg)
 </center>
 
 <!-- prettier-ignore -->
@@ -394,7 +394,7 @@ Finally, you can deploy the trained machine learning model in a production envir
 
 ### Using Argo Workflows
 
-![Argo Workflows](../images/argo-workflows-assembly-line.jpg)
+![Workflow Production Art](../images/argo-workflows-assembly-line.jpg)
 
 <!-- prettier-ignore -->
 !!! info "MLOps Best Practices"

docs/en/7-MLOps/PaaS-Integration.md

@@ -19,7 +19,9 @@ to help!
 
 _Integration is key to success._
 
+<center>
 [![Integrate with PaaS](../images/IntegratePaaS.PNG)]()
+</center>
 
 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.
 

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
 
 <center>
-![image](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png)
+![AAW icon](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png)
 </center>
 
 ### The AAW Portal