canonical · nhennigan · Sep 23, 2026 · Sep 23, 2026
diff --git a/docs/explanation/dss-arch.rst b/docs/explanation/dss-arch.rst
@@ -4,7 +4,7 @@ DSS architecture
 This guide provides an overview of the Data Science Stack (DSS) architecture, its main components, and their interactions. 
 
 DSS is a ready-to-run environment for Machine Learning (ML) and Data Science (DS). 
-It's built on open-source tooling, including `Canonical K8s`_, JupyterLab, and `MLflow <https://ubuntu.com/blog/what-is-mlflow>`_.
+It's built on open-source tooling, including `Canonical Kubernetes`_, JupyterLab, and `MLflow <https://ubuntu.com/blog/what-is-mlflow>`_.
 
 DSS is distributed as a `snap`_ and usable on any Ubuntu workstation. 
 This provides robust security management and user-friendly version control, enabling seamless updates and auto-rollback in case of failure. 
@@ -79,9 +79,9 @@ Orchestration
 ~~~~~~~~~~~~~
 
 DSS requires a container orchestration solution. 
-DSS relies on `Canonical K8s`_, a lightweight Kubernetes distribution.
+DSS relies on `Canonical Kubernetes`_, a lightweight Kubernetes distribution.
 
-Therefore, Canonical K8s needs to be deployed before installing DSS on the host machine. 
+Therefore, Canonical Kubernetes needs to be deployed before installing DSS on the host machine. 
 It must be configured with local storage support to handle persistent volumes used by DSS.
 
 .. _gpu_support:
@@ -93,7 +93,7 @@ DSS can run with or without the use of GPUs.
 If needed, follow `NVIDIA GPU Operator <https://docs.nvidia.com/datacenter/cloud-native/gpu-operator/latest/getting-started.html>`_ for deployment details.
 
 DSS does not automatically install the tools and libraries required for running GPU workloads.
-It relies on Canonical K8s for the required operating-system drivers.
+It relies on Canonical Kubernetes for the required operating-system drivers.
 It also depends on the chosen image, for example, CUDA when working with NVIDIA GPUs.
 
 .. caution::
@@ -103,7 +103,7 @@ Storage
 ^^^^^^^
 
 DSS expects a default `storage class <https://kubernetes.io/docs/concepts/storage/storage-classes/>`_ in the Kubernetes deployment, which is used to persist Jupyter Notebooks and MLflow artefacts.   
-In Canonical K8s, a local storage class should be configured to provision Kubernetes' *PersistentVolumeClaims* (`PVCs <https://kubernetes.io/docs/concepts/storage/persistent-volumes/>`_). 
+In Canonical Kubernetes, a local storage class should be configured to provision Kubernetes' *PersistentVolumeClaims* (`PVCs <https://kubernetes.io/docs/concepts/storage/persistent-volumes/>`_). 
 
 A shared PVC is used across all Jupyter Notebooks to share and persist data. 
 MLflow also uses its dedicated PVC to store the logged artefacts.
@@ -142,7 +142,5 @@ This includes the GPU Operator for managing access and usage.
 Accessibility
 -------------
 
-Jupyter Notebooks and MLflow can be accessed from a web browser through the Pod IP that is given access through Canonical K8s.
+Jupyter Notebooks and MLflow can be accessed from a web browser through the Pod IP that is given access through Canonical Kubernetes.
 See :ref:`access_notebook` and :ref:`access_mlflow` for more details.
-
-.. _Canonical K8s: https://snapcraft.io/k8s
diff --git a/docs/how-to/dss.rst b/docs/how-to/dss.rst
@@ -11,7 +11,7 @@ Install
 -------
 
 .. note::
-   To install DSS, ensure you have previously installed `Snap`_ and `Canonical K8s`_.
+   To install DSS, ensure you have previously installed `Snap`_ and `Canonical Kubernetes`_.
 
 You can install DSS using ``snap`` as follows:
 
@@ -31,15 +31,15 @@ Initialise
 You can initialise DSS through ``dss initialize``.
 This command:
 
-* Stores credentials for the Canonical K8s cluster.
+* Stores credentials for the Canonical Kubernetes cluster.
 * Allocates storage for your DSS Jupyter Notebooks.
 * Deploys an `MLflow <MLflow Docs_>`_ model registry.
 
 .. code-block:: shell
 
     dss initialize --kubeconfig "$(sudo k8s config)"
 
-The ``--kubeconfig`` option is used to provide your Canonical K8s cluster's kubeconfig.
+The ``--kubeconfig`` option is used to provide your Canonical Kubernetes cluster's kubeconfig.
 
 .. note::
    Note the use of quotes for the ``--kubeconfig`` option. Without them, the content may be interpreted by your shell.
@@ -63,7 +63,7 @@ You should expect an output like this:
 Remove
 ------
 
-You can remove DSS from your Canonical K8s cluster through ``dss purge``. 
+You can remove DSS from your Canonical Kubernetes cluster through ``dss purge``. 
 This command purges all the DSS components, including:
 
 * All Jupyter Notebooks.
@@ -72,7 +72,7 @@ This command purges all the DSS components, including:
 
 .. note::
 
-    This action removes the components of the DSS environment, but it does not remove the DSS CLI or your Canonical K8s cluster.  
+    This action removes the components of the DSS environment, but it does not remove the DSS CLI or your Canonical Kubernetes cluster.  
     To remove those, `delete their snaps <https://snapcraft.io/docs/get-started>`_.
 
 .. code-block:: bash

diff --git a/docs/how-to/enable-gpus/enable-intel-gpu.rst b/docs/how-to/enable-gpus/enable-intel-gpu.rst
@@ -5,7 +5,7 @@ Enable Intel GPUs
 
 This guide describes how to configure Data Science Stack (DSS) to utilise the Intel GPUs on your machine. 
 
-You can do so by enabling the Intel device plugin on your `Canonical K8s`_ cluster.
+You can do so by enabling the Intel device plugin on your `Canonical Kubernetes`_ cluster.
 
 Prerequisites
 -------------
@@ -44,7 +44,7 @@ If the drivers are correctly installed, you should see information about your GP
 Enable the Intel GPU plugin
 ---------------------------
 
-To ensure DSS can utilise Intel GPUs, you have to enable the Intel GPU plugin in your Canonical K8s cluster.
+To ensure DSS can utilise Intel GPUs, you have to enable the Intel GPU plugin in your Canonical Kubernetes cluster.
 
 1. Use `kubectl kustomize` to build the plugin YAML configuration files:
 
@@ -61,23 +61,23 @@ To allow multiple containers to utilise the same GPU, run:
 
   sed -i 's/enable-monitoring/enable-monitoring\n        - -shared-dev-num=10/' gpu_plugin.yaml
 
-2. Apply the built YAML files to your Canonical K8s cluster:
+2. Apply the built YAML files to your Canonical Kubernetes cluster:
 
 .. code-block:: bash
 
   kubectl apply -f node_feature_discovery.yaml
   kubectl apply -f node_feature_rules.yaml
   kubectl apply -f gpu_plugin.yaml
 
-The Canonical K8s cluster is now configured to recognise and utilise your Intel GPU.
+The Canonical Kubernetes cluster is now configured to recognise and utilise your Intel GPU.
 
 .. note::
  After the YAML configuration files have been applied, they can be safely deleted.
 
 Verify the Intel GPU plugin
 ---------------------------
 
-To verify the Intel GPU plugin is installed and the Canonical K8s cluster recognises your GPU, run:
+To verify the Intel GPU plugin is installed and the Canonical Kubernetes cluster recognises your GPU, run:
 
 .. code-block:: bash
 

diff --git a/docs/how-to/enable-gpus/enable-nvidia-gpu.rst b/docs/how-to/enable-gpus/enable-nvidia-gpu.rst
@@ -3,7 +3,7 @@
 Enable NVIDIA GPUs
 ==================
 
-This guide describes how to configure Data Science Stack (DSS) to utilise your NVIDIA GPUs within a Canonical K8s environment.
+This guide describes how to configure Data Science Stack (DSS) to utilise your NVIDIA GPUs within a Canonical Kubernetes environment.
 
 DSS supports GPU acceleration by leveraging the `NVIDIA GPU Operator`_. The operator ensures that the necessary components, including drivers and runtime, are set up correctly to enable GPU workloads.
 

diff --git a/docs/index.rst b/docs/index.rst
@@ -5,9 +5,9 @@ Data Science Stack documentation
 ================================
 
 Data Science Stack (DSS) is a ready-to-run environment for Machine Learning (ML) and data science.  
-It's built on open-source tooling, including Canonical K8s, JupyterLab, and MLflow, and is usable on any Ubuntu/Snap-enabled workstation.
+It's built on open-source tooling, including Canonical Kubernetes, JupyterLab, and MLflow, and is usable on any Ubuntu/Snap-enabled workstation.
 
-DSS provides a Command Line Interface (CLI) for managing containerised ML environment images such as PyTorch or TensorFlow, on top of Canonical K8s.
+DSS provides a Command Line Interface (CLI) for managing containerised ML environment images such as PyTorch or TensorFlow, on top of Canonical Kubernetes.
 
 Typically, creating ML environments on a workstation involves complex and hard-to-reverse configurations. 
 DSS solves this problem by providing accessible, production-ready, isolated, and reproducible ML environments that fully utilise a workstation's GPUs.

diff --git a/docs/reuse/links.txt b/docs/reuse/links.txt
@@ -3,7 +3,7 @@
 .. _How to publish documentation on Read the Docs: https://library.canonical.com/documentation/publish-on-read-the-docs
 .. _Example product documentation: https://canonical-example-product-documentation.readthedocs-hosted.com/
 
-.. _Canonical K8s: https://snapcraft.io/k8s
+.. _Canonical Kubernetes: https://snapcraft.io/k8s
 .. _Charmed MLflow: https://documentation.ubuntu.com/charmed-mlflow/en/latest/
 .. _Code of conduct: https://ubuntu.com/community/ethos/code-of-conduct
 .. _Contribute: https://github.com/canonical/data-science-stack/blob/main/CONTRIBUTING.md

diff --git a/docs/tutorial/get-started.rst b/docs/tutorial/get-started.rst
@@ -4,7 +4,7 @@ Get started with DSS
 ====================
 
 This guide describes how you can get started with Data Science Stack (DSS). 
-From setting up Canonical K8s in your host environment, all the way to running your first notebook.
+From setting up Canonical Kubernetes in your host environment, all the way to running your first notebook.
 
 Data Science Stack is a ready-made environment that makes it seamless to run GPU-enabled containerised Machine Learning (ML) environments. 
 It provides easy access to a solution for developing and optimising ML models, utilising your machine's GPUs and allowing users to utilise different ML environment images based on their needs.
@@ -18,14 +18,14 @@ Requirements
 
 .. _set_canonical_k8s:
 
-Set up Canonical K8s
---------------------
+Set up Canonical Kubernetes
+----------------------------
 
 DSS relies on a container orchestration system, capable of exposing the host GPUs to the workloads. 
-`Canonical K8s`_ is used as the orchestration system.
-All the workloads and state managed by DSS are running on top of Canonical K8s.
+`Canonical Kubernetes`_ is used as the orchestration system.
+All the workloads and state managed by DSS are running on top of Canonical Kubernetes.
 
-You can install Canonical K8s using ``snap`` as follows:
+You can install Canonical Kubernetes ``k8s snap`` as follows:
 
 .. code-block:: bash
 
@@ -50,7 +50,7 @@ Now, install the DSS CLI using the following command:
 Initialise DSS
 --------------
 
-Next, you need to initialise DSS on top of Canonical K8s and prepare MLflow:
+Next, you need to initialise DSS on top of Canonical Kubernetes and prepare MLflow:
 
 .. code-block:: bash