Publish with Canadian-made software.
Plotly 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.
diff --git a/en/2-Publishing/Datasette/Datasette.md b/en/2-Publishing/Datasette/Datasette.md index dd62e4d7b..56724c164 100644 --- a/en/2-Publishing/Datasette/Datasette.md +++ b/en/2-Publishing/Datasette/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`. +In your Jupyter Notebook, open a terminal window and run the command
-pip3 install datasette. 
pip3 install datasette.
+
+To view your own database in your Jupyter Notebook, create a file called start.sh in your project directory and copy the below code into it. Make the diff --git a/en/2-Publishing/R-Shiny/R-Shiny.md b/en/2-Publishing/R-Shiny/R-Shiny.md index 126e41f18..52c1382b6 100644 --- a/en/2-Publishing/R-Shiny/R-Shiny.md +++ b/en/2-Publishing/R-Shiny/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/en/2-Publishing/R-Shiny/index.html b/en/2-Publishing/R-Shiny/index.html index bc9ad652e..4e159becb 100644 --- a/en/2-Publishing/R-Shiny/index.html +++ b/en/2-Publishing/R-Shiny/index.html @@ -1513,17 +1513,17 @@
The first thing you'll see is the template chooser. There are three options as of this writing (shinyuieditor is currently in alpha).
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.
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.
All you have to do is send a pull request to our R-Dashboards repository. Include your repository in a folder with the name you want (for example, "air-quality-dashboard"). Then we will approve it and it will come online.
diff --git a/en/3-Pipelines/Argo/Argo.md b/en/3-Pipelines/Argo/Argo.md index 06d92e60a..45ab86835 100644 --- a/en/3-Pipelines/Argo/Argo.md +++ b/en/3-Pipelines/Argo/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 ""
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.
Full documentation can be found here.
Argo Workflows allows you to
@@ -1594,7 +1594,7 @@
+
You can add or remove people from a namespace you already own through the Manage Contributors menu in Kubeflow.
-
Now you and your colleagues can share access to a server!
diff --git a/en/6-Gitlab/Gitlab/Gitlab.md b/en/6-Gitlab/Gitlab/Gitlab.md index 50f941818..633534980 100644 --- a/en/6-Gitlab/Gitlab/Gitlab.md +++ b/en/6-Gitlab/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/en/6-Gitlab/Gitlab/index.html b/en/6-Gitlab/Gitlab/index.html index 6216abff0..905b22a65 100644 --- a/en/6-Gitlab/Gitlab/index.html +++ b/en/6-Gitlab/Gitlab/index.html @@ -1533,25 +1533,25 @@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
-
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
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)
Git to be you¶Run git config user.email .... and git config user.name ... to match your GitLab identity.
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
-
Depending on your use case, either disk or bucket may be most suitable. Our storage overview will help you compare them.
Disks are added to your notebook server by adding Data Volumes.
+
+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.
One of the primary advantages of a data lake is its flexibility. It allows you to store all types of data without the need to define a schema in advance, which is especially useful when dealing with unstructured data. This flexibility allows data scientists to easily explore, experiment, and extract insights from their data without being constrained by the limitations of a traditional relational database.
diff --git a/en/7-MLOps/Machine-Learning-Training-Pipelines/Machine-Learning-Training-Pipelines.md b/en/7-MLOps/Machine-Learning-Training-Pipelines/Machine-Learning-Training-Pipelines.md index 150e9a0b9..8d2fa5817 100644 --- a/en/7-MLOps/Machine-Learning-Training-Pipelines/Machine-Learning-Training-Pipelines.md +++ b/en/7-MLOps/Machine-Learning-Training-Pipelines/Machine-Learning-Training-Pipelines.md @@ -1,7 +1,7 @@ # Training Machine Learning Models on the AAW
+
Finally, you can deploy the trained machine learning model in a production environment.
MLOps Best Practices
diff --git a/en/7-MLOps/PaaS-Integration/PaaS-Integration.md b/en/7-MLOps/PaaS-Integration/PaaS-Integration.md index 36f25b423..5ce7f47f9 100644 --- a/en/7-MLOps/PaaS-Integration/PaaS-Integration.md +++ b/en/7-MLOps/PaaS-Integration/PaaS-Integration.md @@ -19,7 +19,9 @@ to help! _Integration is key to success._ +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.
We can integrate with many Platform as a Service (PaaS) offerings, like Databricks or AzureML.
No matter what stage of your data science journey you're at, the Advanced Analytics Workspace has the resources you need to succeed.
+
The AAW portal homepage is available for internal users only. However, external users with a cloud account granted access by the business sponsor can access the platform through the analytics-platform URL.
diff --git a/en/index.md b/en/index.md index dab50f0a7..96236e6f4 100644 --- a/en/index.md +++ b/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