StatCan · wg102 · Oct 17, 2023 · Oct 13, 2023 · Oct 13, 2023 · Oct 13, 2023
diff --git a/docs/en/1-Experiments/Jupyter.md b/docs/en/1-Experiments/Jupyter.md
@@ -20,7 +20,7 @@ Jupyter comes with a number of features (and we can add more)
 [![Explore your data](../images/ExploreData.PNG)](../../2-Publishing/Datasette/)
 </center>
 
-Use **[Datasette](~../2-Publishing/Datasette.md/)** , an instant JSON API for your SQLite databases. Run SQL queries in a more interactive way!
+Use **[Datasette](../../2-Publishing/Datasette.md/)** , an instant JSON API for your SQLite databases. Run SQL queries in a more interactive way!
 
 ### IDE in the browser
 
@@ -78,9 +78,9 @@ You can upload and download data to/from JupyterHub directly in the menu. There
 
 ### Shareable "Bucket" storage
 
-There is also a mounted `buckets` folder in your home directory, which holds files in [MinIO](../Storage.md/#buckets-via-minio).
+There is also a mounted `buckets` folder in your home directory, which holds files in [Azure Blob Storage](../../5-Storage/AzureBlobStorage).
 
-**Refer to the [Storage](../index.md#storage) section for details.**
+**Refer to the [Storage](../../5-Storage/Overview) section for details.**
 
 ## Data Analysis
 

diff --git a/docs/en/1-Experiments/Remote-Desktop.md b/docs/en/1-Experiments/Remote-Desktop.md
@@ -14,7 +14,7 @@ The Ubuntu Virtual Desktop is a powerful tool for data scientists and machine le
 
 Remote Desktop provides an in-browser GUI Ubuntu desktop experience as well as
 quick access to supporting tools. The operating system is
-[**Ubuntu**](https://ubuntu.com/about) **18.04** with the
+[**Ubuntu**](https://ubuntu.com/about) **22.04** with the
 [**XFCE**](https://www.xfce.org/about) desktop environment.
 
 ![Remote Desktop](../images/rd_desktop.png)
@@ -32,7 +32,7 @@ _pip_, _conda_, _npm_ and _yarn_ are available to install various packages.
 ## Accessing the Remote Desktop
 
 To launch the Remote Desktop or any of its supporting tools, create a Notebook
-Server in [Kubeflow](./Kubeflow.md) and select the remote desktop option.
+Server in [Kubeflow](./Kubeflow.md) and select the remote desktop option, which is the Ubuntu image.
 
 ![Remote Desktop](../images/RemoteDesktop.PNG)
 

diff --git a/docs/en/1-Experiments/Selecting-an-Image.md b/docs/en/1-Experiments/Selecting-an-Image.md
@@ -8,7 +8,7 @@ When selecting an image, you have 3 main options:
 
 - Jupyter Notebook (CPU, TensorFlow, PyTorch)
 - RStudio
-- Remote Desktop (r, geomatics)
+- Remote Desktop
 
 ## Jupyter Notebooks
 
@@ -48,7 +48,7 @@ experience.
 
 ## RStudio
 
-**[RStudio](RStudio/)** gives you an integrated development environment
+**[RStudio](../RStudio/)** gives you an integrated development environment
 specifically for `R`. If you're coding in `R`, this is typically the Notebook
 Server to use. Use the `rstudio` image to get an RStudio environment.
 
@@ -59,7 +59,7 @@ Server to use. Use the `rstudio` image to get an RStudio environment.
 For a full Ubuntu desktop experience, use the remote desktop image. It comes
 pre-loaded with Python, R and Geomatics tooling, but are delivered in a typical
 desktop experience that also comes with Firefox, VS Code, and open office tools.
-The operating system is **[Ubuntu](https://ubuntu.com/about)** 18.04 with the
+The operating system is **[Ubuntu](https://ubuntu.com/about)** 22.04 with the
 **[XFCE](https://www.xfce.org/about)** desktop environment.
 
 ![Screenshot of the Virtual Desktop](../images/rd_desktop.png)
diff --git a/docs/en/2-Publishing/Accessing-Published-Content.md b/docs/en/2-Publishing/Accessing-Published-Content.md
diff --git a/docs/en/2-Publishing/Custom.md b/docs/en/2-Publishing/Custom.md
@@ -9,9 +9,7 @@ container. For instance, Node.js apps, Flask or Dash apps. Etc.
 
 <!-- prettier-ignore -->
 !!! info "See the source code for this app"
-    We just push these kinds of applications through GitHub into the server. The
-    source for the above app is
-    [`StatCan/covid19`](https://github.com/StatCan/covid19)
+    We just push these kinds of applications through GitHub into the server.
 
 # Setup
 

diff --git a/docs/en/2-Publishing/Dash.md b/docs/en/2-Publishing/Dash.md
@@ -31,7 +31,7 @@ This is an example of a Layout With Figure and Slider from
 
 _Publish with Canadian-made software._
 
-**[Plotly Dash](/2-Publishing/Dash/)** is a popular Python library that allows you to create interactive web-based visualizations and dashboards with ease. Developed by the Montreal-based company Plotly, Dash has gained a reputation for being a powerful and flexible tool for building custom data science graphics. With Dash, you can create everything from simple line charts to complex, multi-page dashboards with interactive widgets and controls. Because it's built on open source technologies like Flask, React, and Plotly.js, Dash is highly customizable and can be easily integrated with other data science tools and workflows. Whether you're a data scientist, analyst, or developer, Dash can help you create engaging and informative visualizations that bring your data to life.
+**[Plotly Dash](../Dash/)** is a popular Python library that allows you to create interactive web-based visualizations and dashboards with ease. Developed by the Montreal-based company Plotly, Dash has gained a reputation for being a powerful and flexible tool for building custom data science graphics. With Dash, you can create everything from simple line charts to complex, multi-page dashboards with interactive widgets and controls. Because it's built on open source technologies like Flask, React, and Plotly.js, Dash is highly customizable and can be easily integrated with other data science tools and workflows. Whether you're a data scientist, analyst, or developer, Dash can help you create engaging and informative visualizations that bring your data to life.
 
 # Getting Started
 

diff --git a/docs/en/2-Publishing/Datasette.md b/docs/en/2-Publishing/Datasette.md
@@ -26,9 +26,10 @@ You can even explore maps within the tool!
 
 ![Run SQL Queries](../images/datasette-sql.png)
 
+<!-- Removing until video is approved
 # Video Tutorial
 
-[![Click here for the video](../images/KubeflowVideo.PNG)](https://youtu.be/OPVfBKouBT8?t=214 "Advanced Analytics Workspace Kubeflow collaboration demo + tips and tricks")
+[![Click here for the video](../images/KubeflowVideo.PNG)](https://youtu.be/OPVfBKouBT8?t=214 "Advanced Analytics Workspace Kubeflow collaboration demo + tips and tricks") -->
 
 # Getting Started
 

diff --git a/docs/en/2-Publishing/PowerBI.md b/docs/en/2-Publishing/PowerBI.md
@@ -13,7 +13,7 @@ our Storage system, and use the data as a `pandas` data frame.
 
 1. A computer with Power BI, and Python 3.6
 2. Your MinIO `ACCESS_KEY` and `SECRET_KEY` on hand. (See
-   [Storage](../index.md#storage))
+   [Storage](../../5-Storage/Overview))
 
 ## Set up Power BI
 

diff --git a/docs/en/2-Publishing/R-Shiny.md b/docs/en/2-Publishing/R-Shiny.md
@@ -12,7 +12,7 @@ R-Shiny is an R package that makes it easy to build interactive web apps in R.
 
 _Publish Professional Quality Graphics_
 
-[![InteractiveDashboard](../images/InteractiveDashboard.PNG)](/2-Publishing/R-Shiny/)
+[![InteractiveDashboard](../images/InteractiveDashboard.PNG)](../R-Shiny/)
 
 R Shiny is an open source web application framework that allows data scientists and analysts to create interactive, web-based dashboards and data visualizations using the R programming language. One of the main advantages of R Shiny is that it offers a straightforward way to create high-quality, interactive dashboards without the need for extensive web development expertise. With R Shiny, data scientists can leverage their R coding skills to create dynamic, data-driven web applications that can be shared easily with stakeholders.
 
@@ -109,7 +109,7 @@ If you need extra R libraries to be installed, send your list to [the R-Shiny re
 
 <!-- prettier-ignore -->
 !!! example "See the above dashboard here"
-    The above dashboard is in GitHub. Take a look at [the source](https://github.com/StatCan/R-dashboards/tree/master/bus-dashboard), and [see the dashboard live](https://shiny.covid.cloud.statcan.ca/bus-dashboard).
+    The above dashboard is in GitHub. Take a look at [the source](https://github.com/StatCan/R-dashboards/tree/master/bus-dashboard)).
 
 ## Once you've got the basics ...
 

diff --git a/docs/en/3-Pipelines/Argo.md b/docs/en/3-Pipelines/Argo.md
@@ -374,14 +374,6 @@ Couler provides a simple, unified application programming interface for defining
     # Run the workflow
     w.create()
     ```
-=== "YAML"
-    ``` py title="workflow.yaml" linenums="1"
-
-    ```
-=== "Seldon?"
-    ``` py
-
-    ```
 
 ### Additional Resources for Argo Workflows
 

diff --git a/docs/en/4-Collaboration/Overview.md b/docs/en/4-Collaboration/Overview.md
@@ -10,7 +10,7 @@ There are many ways collaborate on the AAW. Which is best for your situation dep
 |  **Data**   |    Personal folder or bucket     | Team folder or bucket, or shared namespace | Shared Bucket |
 | **Compute** |        Personal namespace        |              Shared namespace              |      N/A      |
 
-Sharing code, disks, and workspaces (e.g.: two people sharing the same virtual machine) is described in more detail below. Sharing data through buckets is described in more detail in the **[MinIO](../5-Storage/AzureBlobStorage.md)** section.
+Sharing code, disks, and workspaces (e.g.: two people sharing the same virtual machine) is described in more detail below. Sharing data through buckets is described in more detail in the **[Azure Blob Storage](../5-Storage/AzureBlobStorage.md)** section.
 
 <!-- prettier-ignore -->
 ??? question "What is the difference between a bucket and a folder?"
@@ -37,7 +37,7 @@ If you need to share code without publishing it on a repository,
 
 <!-- prettier-ignore -->
 !!! danger "Sharing a namespace means you share **everything** in the namespace"
-    Kubeflow does not support granular sharing of one resource (one notebook, one MinIO bucket, etc.), but instead sharing of **all** resources. If you want to share a Jupyter Notebook server with someone, you must share your entire namespace and **they will have access to all other resources (MinIO buckets, etc.)**.
+    Kubeflow does not support granular sharing of one resource (one notebook, one volume, etc.), but instead sharing of **all** resources. If you want to share a Jupyter Notebook server with someone, you must share your entire namespace and **they will have access to all other resources (Azure Blob Storage, etc.)**.
 
 In Kubeflow every user has a **namespace** that contains their work (their
 notebook servers, pipelines, disks, etc.). Your namespace belongs to you, but
@@ -47,7 +47,7 @@ share with a team). One option for collaboration is to share namespaces with
 others.
 
 The advantage of sharing a Kubeflow namespace is that it lets you and your
-colleagues share the compute environment and MinIO buckets associated with the
+colleagues share the compute environment and  volumes associated with the
 namespace. This makes it a very easy and free-form way to share.
 
 To share your namespace, see [managing contributors](#managing-contributors)
@@ -68,30 +68,13 @@ Once you have a shared namespace, you have two shared storage approaches
 To learn more about the technology behind these, check out the
 [Storage overview](../5-Storage/Overview.md).
 
-### Sharing with StatCan
-
-In addition to private buckets, or team-shared private buckets, you can also
-place your files in _shared storage_. Within all bucket storage options
-(`minimal`, `premium`, `pachyderm`), you have a private bucket, **and** a folder
-inside of the `shared` bucket. Take a look, for instance, at the link below:
-
-- [`shared/blair-drummond/`](https://minimal-tenant1-minio.covid.cloud.statcan.ca/minio/shared/blair-drummond/)
-
-Any **logged in** user can see these files and read them freely.
-
 ### Sharing with the world
 
 Ask about that one in our [Slack channel](https://statcan-aaw.slack.com). There
 are many ways to do this from the IT side, but it's important for it to go
 through proper processes, so this is not done in a "self-serve" way that the
 others are. That said, it is totally possible.
 
-## Recommendation: Combine them all
-
-It's a great idea to always use git, and using git along with shared workspaces
-is a great way to combine ad hoc sharing (through files) while also keeping your
-code organized and tracked.
-
 ## Managing contributors
 
 You can add or remove people from a namespace you already own through the

diff --git a/docs/en/5-Storage/Disks.md b/docs/en/5-Storage/Disks.md
@@ -6,7 +6,7 @@ you from fast solid state drives (SSDs)!
 # Setup
 
 When creating your notebook server, you request disks by adding Data Volumes to
-your notebook server (pictured below, with `Type = New`). They are automatically
+your notebook server (pictured below, with go to `Advanced Options`). They are automatically
 mounted at the directory (`Mount Point`) you choose, and serve as a simple and
 reliable way to preserve data attached to a Notebook Server.
 
@@ -27,7 +27,7 @@ to reuse). If you're done with the disk and it's contents,
 ## Deleting Disk Storage
 
 To see your disks, check the Notebook Volumes section of the Notebook Server
-page (shown below). You can delete any unattached disk (orange icon on the left)
+page (shown below). You can delete any unattached disk (icon on the left)
 by clicking the trash can icon.
 
 ![Delete an unattached volume from the Notebook Server screen](../images/kubeflow_delete_disk.png)

diff --git a/docs/en/7-MLOps/Machine-Learning-Model-Cloud-Storage.md b/docs/en/7-MLOps/Machine-Learning-Model-Cloud-Storage.md
@@ -29,25 +29,16 @@ Overall, cloud storage is a reliable and convenient solution for storing and man
 The AAW platform provides several types of storage:
 
 - Disks (also called Volumes on the Kubeflow Notebook Server creation screen)
-- Buckets ("Blob" or S3 storage, provided through MinIO)
 - Data Lakes (coming soon)
 
 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)](Storage.md/)
+[![Disks](../images/Disks.PNG)](../5-Storage/Disks.md)
 
 **[Disks](../5-Storage/Disks.md)** are added to your notebook server by adding Data Volumes.
 
-### Buckets
-
-MinIO is an S3-API compatible object storage system that provides an open source alternative to proprietary cloud storage services. While we currently use MinIO as our cloud storage solution, we plan on replacing it with s3-proxy in the near future. S3-proxy is a lightweight, open source reverse proxy server that allows you to access Amazon S3-compatible storage services with your existing applications. By switching to s3-proxy, we will be able to improve our cloud storage performance, security, and scalability, while maintaining compatibility with the S3 API.
-
-[![MinIO](../images/Buckets.PNG)](AzureBlobStorage.md/)
-
-**[MinIO](../5-Storage/AzureBlobStorage.md)** is a cloud-native scalable object store. We use it for buckets (blob or S3 storage).
-
 ### Data Lakes (Coming Soon)
 
 A data lake is a central repository that allows you to store all your structured and unstructured data at any scale. It's a cost-effective way to store and manage all types of data, from raw data to processed data, and it's an essential tool for data scientists.

diff --git a/docs/en/7-MLOps/Machine-Learning-Training-Pipelines.md b/docs/en/7-MLOps/Machine-Learning-Training-Pipelines.md
@@ -388,44 +388,10 @@ With the data split, you can now define and train your machine learning model us
 
 After training the model, you need to evaluate its performance on the testing set. This will give you an idea of how well the model will perform on new, unseen data.
 
-=== "Python"
-    ``` py title="evaluate.py" linenums="1"
-
-    ```
-=== "R"
-    ``` r title="evaluate.R" linenums="1"
-
-    ```
-=== "SASPy"
-    ``` py title="evaluate.py" linenums="1"
-
-    ```
-=== "SAS"
-    ``` sas title="evaluate.sas" linenums="1"
-
-    ```
-
 #### 6. Deploy the model
 
 Finally, you can deploy the trained machine learning model in a production environment.
 
-=== "Python"
-    ``` py title="deploy.py" linenums="1"
-
-    ```
-=== "R"
-    ``` r title="deploy.R" linenums="1"
-
-    ```
-=== "SASPy"
-    ``` py title="deploy.py" linenums="1"
-
-    ```
-=== "SAS"
-    ``` sas title="deploy.sas" linenums="1"
-
-    ```
-
 ### Using Argo Workflows
 
 ![Argo Workflows](../images/argo-workflows-assembly-line.jpg)

diff --git a/docs/en/Help.md b/docs/en/Help.md
@@ -10,13 +10,13 @@ channel. You can ask questions and provide feedback there.
 
 We will also post notices there if there are updates or downtime.
 
-# Video tutorials
+<!-- # Video tutorials
 
 After you have joined our Slack community, go and check out the following
 tutorials:
 
 - [Platform official](https://www.youtube.com/playlist?list=PL1zlA2D7AHugkDdiyeUHWOKGKUd3MB_nD)
-- [Community driven content](https://www.youtube.com/playlist?list=PL1zlA2D7AHuhP0lKbcaD_0KEYUqs1Qrgj)
+- [Community driven content](https://www.youtube.com/playlist?list=PL1zlA2D7AHuhP0lKbcaD_0KEYUqs1Qrgj) -->
 
 # GitHub
 

diff --git a/docs/en/images/RStudioOption.PNG b/docs/en/images/RStudioOption.PNG
diff --git a/docs/en/images/RemoteDesktop.PNG b/docs/en/images/RemoteDesktop.PNG
diff --git a/docs/en/images/kubeflow_contributors.png b/docs/en/images/kubeflow_contributors.png
diff --git a/docs/en/images/kubeflow_delete_disk.png b/docs/en/images/kubeflow_delete_disk.png
diff --git a/docs/en/images/kubeflow_existing_volume.png b/docs/en/images/kubeflow_existing_volume.png
diff --git a/docs/en/images/kubeflow_volumes_disk.png b/docs/en/images/kubeflow_volumes_disk.png
diff --git a/docs/fr/images/kubeflow_contributors.png b/docs/fr/images/kubeflow_contributors.png
diff --git a/docs/fr/images/kubeflow_delete_disk.png b/docs/fr/images/kubeflow_delete_disk.png