rearrange studio links (#5368)

skshetry · web-flow · commit 13bb82086799 · 2025-05-15T19:45:48.000+05:45
* rearrange studio links

* remove unused alert component

* drop gatsby-source-rss-feed
diff --git a/content/docs/command-reference/exp/push.md b/content/docs/command-reference/exp/push.md
@@ -41,7 +41,7 @@ to see experiments in the remote.
 This command will also try to `dvc push` all <abbr>cached</abbr> data associated
 with the experiments to [remote storage], unless `--no-cache` is used.
 
-If the [DVC Studio](https://studio.datachain.ai)
+If the DVC Studio
 [access token](/doc/studio/user-guide/experiments/live-metrics-and-plots#set-up-an-access-token)
 is set, DVC will notify DVC Studio about new experiments, and display a DVC
 Studio project URL to view experiments.
diff --git a/content/docs/command-reference/studio/index.md b/content/docs/command-reference/studio/index.md
@@ -1,7 +1,6 @@
 # studio
 
-A set of commands to authenticate DVC with [Studio](https://studio.datachain.ai)
-and save a
+A set of commands to authenticate DVC with Studio and save a
 [client access token](/doc/studio/user-guide/account-management#client-access-tokens)
 to global [DVC configuration]: [login](/doc/command-reference/studio/login),
 [logout](/doc/command-reference/studio/logout),
diff --git a/content/docs/dvclive/index.md b/content/docs/dvclive/index.md
@@ -120,7 +120,7 @@ See `Live.next_step()`.
 Under the hood, `Live.next_step()` calls `Live.make_summary()`,
 `Live.make_dvcyaml()`, and `Live.make_report()`.
 
-When [access is enabled], updates will be sent to [DVC Studio].
+When [access is enabled], updates will be sent to DVC Studio.
 
 If you want to decouple the `step` update from the rest of the calls, you can
 manually modify the `Live.step` property and call `Live.make_summary()` /
@@ -172,4 +172,3 @@ parametrize your code. See how to [setup a pipeline] to work with DVCLive.
 [run experiments]: /doc/user-guide/experiment-management/running-experiments
 [setup a pipeline]: /doc/dvclive/how-it-works#setup-to-run-with-dvc
 [access is enabled]: /doc/start/experiments/experiment-tracking#sharing
-[dvc studio]: https://studio.datachain.ai
diff --git a/content/docs/dvclive/live/log_image.md b/content/docs/dvclive/live/log_image.md
@@ -92,7 +92,7 @@ with Live() as live:
       live.next_step()
 ```
 
-In [DVC Studio](https://studio.datachain.ai/) and the
+In DVC Studio and the
 [DVC Extension for VSCode](https://marketplace.visualstudio.com/items?itemName=Iterative.dvc),
 folders following this pattern will be rendered using an image slider:
 
diff --git a/content/docs/dvclive/ml-frameworks/pytorch-lightning.md b/content/docs/dvclive/ml-frameworks/pytorch-lightning.md
@@ -93,7 +93,7 @@ checkpointing at all as described in the
 Use `log_model` to save the checkpoints (it will use `Live.log_artifact()`
 internally to save those). At the end of training, DVCLive will copy the
 [`best_model_path`][`ModelCheckpoint`] to the `dvclive/artifacts` directory and
-annotate it with name `best` (for example, to be consumed in [DVC Studio]
+annotate it with name `best` (for example, to be consumed in DVC Studio
 <abbr>model registry</abbr> or automation scenarios).
 
 - Save updates to the checkpoints directory at the end of training:
@@ -188,4 +188,3 @@ dvclive/metrics/train/epoch/metric.tsv
 [`live`]: /doc/dvclive/live
 [`ModelCheckpoint`]:
   https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.ModelCheckpoint.html
-[DVC Studio]: https://studio.datachain.ai
diff --git a/content/docs/start/experiments/experiment-tracking.md b/content/docs/start/experiments/experiment-tracking.md
@@ -131,8 +131,8 @@ want to share results with others or back them up. Optionally follow this
 section to share, or skip to the next section if you want to start locally.
 
 DVC tracks the code and metadata for each experiment in Git. If you have a Git
-remote (for example, GitHub) where you `git push`, go to
-[DVC Studio](https://studio.datachain.ai), configure your Git provider, and
+remote (for example, GitHub) where you `git push`, go to DVC Studio, configure
+your Git provider, and
 [add your Git repository as a project](/doc/studio/user-guide/experiments/create-a-project).
 
 Next, you can login to Studio from the command line:
@@ -260,4 +260,3 @@ Learn more about
 
 [dvclive]: /doc/dvclive
 [shared]: /doc/user-guide/experiment-management/sharing-experiments
-[dvc studio]: https://studio.datachain.ai
diff --git a/content/docs/start/model-registry/manage-models.md b/content/docs/start/model-registry/manage-models.md
@@ -7,8 +7,8 @@ description:
 
 # Get Started: Manage models
 
-In this guide, we will be using [DVC Studio](https://studio.datachain.ai) to
-manage our model registry. You can check out our
+In this guide, we will be using DVC Studio to manage our model registry. You can
+check out our
 [example model](https://studio.datachain.ai/team/Iterative/models/b3P4bcYIrGYdzyjqzsf9Xw==/pool-segmentation/v1.0.0)
 in DVC Studio to see what it will look like once we finish all the steps in this
 guide.
@@ -17,8 +17,8 @@ guide.
 
 To perform the model registry actions in this guide, follow
 [these steps](/doc/studio/user-guide/experiments/create-a-project#connect-to-a-git-repository-and-add-a-project)
-to sign in to [DVC Studio](https://studio.datachain.ai), connect it to your Git
-account and add your repository as a DVC Studio project.
+to sign in to DVC Studio, connect it to your Git account and add your repository
+as a DVC Studio project.
 
 </admon>
 
diff --git a/content/docs/studio/index.md b/content/docs/studio/index.md
@@ -1,7 +1,7 @@
 # DVC Studio
 
-[DVC Studio](https://studio.datachain.ai/) is a web application that enables
-Machine Learning teams to seamlessly
+DVC Studio is a web application that enables Machine Learning teams to
+seamlessly
 
 - [Run and track experiments](/doc/use-cases/experiment-tracking)
 - [Manage model lifecycle](/doc/use-cases/model-registry)
@@ -11,9 +11,8 @@ It works on top of [DVC](https://dvc.org/) and [Git](https://git-scm.com/),
 maintaining Git as the single-source-of-truth for your data, experiments and
 models.
 
-Sign in to [DVC Studio](https://studio.datachain.ai/) using your GitHub.com,
-GitLab.com, or Bitbucket.org account, or with your email address. Explore the
-demo projects and models, and
+Sign in to DVC Studio using your GitHub.com, GitLab.com, or Bitbucket.org
+account, or with your email address. Explore the demo projects and models, and
 [let us know](/doc/studio/user-guide/troubleshooting#support) if you need any
 help getting started.
 
diff --git a/content/docs/studio/user-guide/account-management/index.md b/content/docs/studio/user-guide/account-management/index.md
@@ -153,7 +153,7 @@ granting full access to your Studio account.
 The available scopes are:
 
 - `Experiment operations` - DVC uses this scope to share [live experiments] and
-  to notify [Studio](https://studio.datachain.ai/) about [pushed experiments].
+  to notify Studio about [pushed experiments].
 - `Dataset operations` - [Coming soon](https://cloud.dvc.ai).
 - `Model registry operations` - like downloading model using
   `dvc artifacts get`.
diff --git a/content/docs/studio/user-guide/experiments/create-a-project.md b/content/docs/studio/user-guide/experiments/create-a-project.md
@@ -10,8 +10,8 @@ In this section, you will learn how to:
 
 To add a new project, follow these steps.
 
-1. Sign in to [DVC Studio](https://studio.datachain.ai/) using your GitHub.com,
-   GitLab.com, or Bitbucket.org account, or with your email address.
+1. Sign in to DVC Studio using your GitHub.com, GitLab.com, or Bitbucket.org
+   account, or with your email address.
 
 2. Click on `Add a Project`. All the organizations that you have access to will
    be listed.
diff --git a/content/docs/studio/user-guide/troubleshooting.md b/content/docs/studio/user-guide/troubleshooting.md
@@ -5,11 +5,10 @@ DVC Studio.
 
 ## Support
 
-If you need further help, you can send us a message using `Help` on the
-[DVC Studio website](https://studio.datachain.ai). You can also
-[email us](mailto:support@iterative.ai), create a support ticket on
-[GitHub](https://github.com/iterative/studio-support) or join the discussion in
-[Discord](https://discord.com/invite/dvwXA2N).
+If you need further help, you can send us a message using `Help` on the DVC
+Studio website. You can also [email us](mailto:support@iterative.ai), create a
+support ticket on [GitHub](https://github.com/iterative/studio-support) or join
+the discussion in [Discord](https://discord.com/invite/dvwXA2N).
 
 ## Projects and experiments
 
diff --git a/content/docs/use-cases/data-registry/tutorial.md b/content/docs/use-cases/data-registry/tutorial.md
@@ -187,7 +187,7 @@ Now you know how to to build a lightweight data registry, update it, and get
 files from it. As your registry or team continues to grow, you may have trouble
 managing all artifacts across multiple projects. How do you keep them organized,
 or know which version to use, or share them with others outside your team? DVC
-along with [DVC Studio] can help you scale your registry and address these
+along with DVC Studio can help you scale your registry and address these
 questions.
 
 ### Adding metadata
@@ -209,9 +209,9 @@ artifacts:
 ```
 
 Once you `git commit` and `git push` this info to a project that's connected to
-[DVC Studio], anyone on your team can see it and filter or search across all
-your projects in the <abbr>model registry</abbr>. Although the artifact above is
-not a model, you can change the filters to use it for any type of artifact:
+DVC Studio, anyone on your team can see it and filter or search across all your
+projects in the <abbr>model registry</abbr>. Although the artifact above is not
+a model, you can change the filters to use it for any type of artifact:
 
 ![Show Registry Datasets](https://static.iterative.ai/img/registry-show-datasets.gif)
 
@@ -232,13 +232,12 @@ actions in your CI/CD workflows when you register a version or assign a stage.
 
 Others can [download or stream artifacts] by their version or stage without
 needing access to your Git repository or cloud storage. If you connect your
-[cloud credentials] in [DVC Studio], anyone on your team can access that
-artifact using only a [Studio token], either in the UI or programmatically:
+[cloud credentials] in DVC Studio, anyone on your team can access that artifact
+using only a [Studio token], either in the UI or programmatically:
 
 ![Download Registry Datasets](https://static.iterative.ai/img/registry-download-datasets.gif)
 
 [remote storage]: /doc/user-guide/data-management/remote-storage
-[DVC Studio]: https://studio.datachain.ai
 [Version numbers]: /doc/studio/user-guide/model-registry/register-version
 [stages]: /doc/studio/user-guide/model-registry/assign-stage
 [semantic versioning]: https://semver.org
diff --git a/content/docs/use-cases/model-registry.md b/content/docs/use-cases/model-registry.md
@@ -2,9 +2,9 @@
 
 A **model registry** is a tool to catalog ML models and their versions. Models
 from your data science projects can be discovered, tested, shared, deployed, and
-audited from there. [DVC Studio] model registry enables these capabilities [on
-top of Git][gitops], so you can stick to an existing software engineering stack.
-No more division between ML engineering and operations!
+audited from there. DVC Studio model registry enables these capabilities [on top
+of Git][gitops], so you can stick to an existing software engineering stack. No
+more division between ML engineering and operations!
 
 ![](/img/ml_model_registry.jpg) _MLOps from modeling to production_
 
@@ -43,7 +43,6 @@ supporting all stages of ML model lifecycle.
 To begin with this integrated approach to managing your ML models, [start
 managing models] with DVC!
 
-[DVC Studio]: https://studio.datachain.ai
 [gitops]: https://www.gitops.tech/
 [versions]: /doc/use-cases/versioning-data-and-models
 [start managing models]: /doc/start/model-registry
diff --git a/content/docs/user-guide/data-management/discovering-and-accessing-data.md b/content/docs/user-guide/data-management/discovering-and-accessing-data.md
@@ -142,7 +142,7 @@ path.
 
 <admon type="tip">
 
-The [DVC Studio] model registry was built for models but since DVC tracks all
+The DVC Studio model registry was built for models but since DVC tracks all
 kinds of files, it can be used just as easily for other artifact types. See our
 [tutorial] for how to manage artifacts using the registry.
 
@@ -157,5 +157,4 @@ Studio], you can see models and their metadata across all projects, and you can
 download artifacts by name, version, and lifecycle stage, without needing to
 configure access to the underlying Git repository or remote storage.
 
-[DVC Studio]: https://studio.datachain.ai
 [tutorial]: /doc/use-cases/data-registry/tutorial#sharing-and-managing-artifacts
diff --git a/content/docs/user-guide/env.md b/content/docs/user-guide/env.md
@@ -7,7 +7,7 @@ List of environment variables to configure DVC behavior.
 - `DVC_EXP_BASELINE_REV`: Git revision for the baseline commit from which an
   <abbr>experiment</abbr> derives. Automatically set by DVC.
 - `DVC_EXP_GIT_REMOTE`: Git remote name or URL used to [push the experiments]
-  and [send live metrics and plots] to [DVC Studio]. If not specified, push to
+  and [send live metrics and plots] to DVC Studio. If not specified, push to
   `origin`. Overrides `dvc config exp.git_remote`.
 - `DVC_EXP_NAME`: Name of the <abbr>experiment</abbr>. Automatically set by DVC.
 - `DVC_GLOBAL_CONFIG_DIR`: Directory in which DVC will look for global
@@ -24,9 +24,9 @@ List of environment variables to configure DVC behavior.
 - `DVC_STUDIO_OFFLINE`: If `true`, disables sharing
   [live experiments](/doc/studio/user-guide/experiments/live-metrics-and-plots)
   even if the DVC Studio token is set. Overrides `dvc config studio.offline`.
-- `DVC_STUDIO_TOKEN`: Set [DVC Studio] access token to use. Overrides
+- `DVC_STUDIO_TOKEN`: Set DVC Studio access token to use. Overrides
   `dvc config studio.token`.
-- `DVC_STUDIO_URL`: Set URL of [DVC Studio] to use (in case of self-hosted DVC
+- `DVC_STUDIO_URL`: Set URL of DVC Studio to use (in case of self-hosted DVC
   Studio instance). Overrides `dvc config studio.url`.
 - `DVC_SYSTEM_CONFIG_DIR`: Directory in which DVC will look for system
   [configuration](/doc/user-guide/project-structure/configuration).
@@ -39,4 +39,3 @@ See also [DVCLive environment variables](/doc/dvclive/env).
   /doc/user-guide/experiment-management/sharing-experiments#push-experiments
 [send live metrics and plots]:
   /doc/studio/user-guide/experiments/live-metrics-and-plots
-[dvc studio]: https://studio.datachain.ai
diff --git a/content/docs/user-guide/experiment-management/comparing-experiments.md b/content/docs/user-guide/experiment-management/comparing-experiments.md
@@ -287,7 +287,7 @@ files, etc.) can be stored in Git.
 The methods above let you compare experiments from your local machine where you
 are working without any servers, logins, or infrastructure. However, sometimes
 you are away from your machine, or you want to share the results with others.
-[DVC Studio] provides a web collaboration hub for [sharing experiments] and
+DVC Studio provides a web collaboration hub for [sharing experiments] and
 [visualizing and comparing experiments] for your entire project history.
 
 ![DVC Studio Compare Experiments Overview](/img/studio-compare-exps-overview.gif)
@@ -335,7 +335,6 @@ artifacts, etc.) related with the experiments, you can use `dvc gc`.
 
 [dvc extension for vs code]:
   https://marketplace.visualstudio.com/items?itemName=Iterative.dvc
-[dvc studio]: https://studio.datachain.ai
 [sharing experiments]: /doc/user-guide/experiment-management/sharing-experiments
 [visualizing and comparing experiments]:
   /doc/studio/user-guide/experiments/visualize-and-compare
diff --git a/content/docs/user-guide/experiment-management/sharing-experiments.md b/content/docs/user-guide/experiment-management/sharing-experiments.md
@@ -12,7 +12,7 @@ automatically share all experiment info, you will need a:
 
 - Git remote (for example, GitHub)
 - DVC [remote storage] for any large artifacts you want DVC to track
-- [DVC Studio] project [connected to that Git remote]
+- DVC Studio project [connected to that Git remote]
 
 To automatically share your experiments, run:
 
@@ -22,7 +22,7 @@ $ dvc studio login
 
 Once configured, DVC will:
 
-- send [live metrics and plots] to [DVC Studio]
+- send [live metrics and plots] to DVC Studio
 - push experiment code and metadata to your Git repo
 - push data, models, and other artifacts to your DVC [remote storage]
 
@@ -38,9 +38,9 @@ metrics and plots.
 
 </admon>
 
-You can send [live experiments] to [DVC Studio], which will show intermediate
+You can send [live experiments] to DVC Studio, which will show intermediate
 results for metrics and plots in any running experiments. To start sharing live
-metrics to [DVC Studio], set your [access token].
+metrics to DVC Studio, set your [access token].
 
 While the experiment runs, you will see live updates like this in DVC Studio
 (and so will anyone else with access to the project):
@@ -169,7 +169,7 @@ default value, set the configuration option `exp.git_remote` or the
 
 ## Find pushed experiments
 
-You can see pushed experiments in [DVC Studio]. From there, you can make an
+You can see pushed experiments in DVC Studio. From there, you can make an
 experiment [persistent] by creating a Git branch, or you can [remove] it from
 your Git remote:
 
@@ -224,7 +224,7 @@ the flags to select a different set of experiments to push.
 DVC experiments run outside of the regular Git workflow for faster iteration and
 to avoid polluting your <abbr>repository</abbr>'s history, but you can easily
 bring back the most promising experiments into your regular Git workflow. You
-can convert any pushed experiment from [DVC Studio] into a persistent Git branch
+can convert any pushed experiment from DVC Studio into a persistent Git branch
 and create a pull request to merge it into your main repo branch:
 
 ![DVC Studio Create a New Branch](/img/studio-branch.gif)
@@ -260,7 +260,7 @@ results to your workspace].
 
 ## Remove pushed experiments
 
-As you share more experiments, [DVC Studio] and Git remotes may be become
+As you share more experiments, DVC Studio and Git remotes may be become
 cluttered with experiment references.
 
 You can remove experiments in DVC Studio:
@@ -276,7 +276,6 @@ Removed experiments: unwet-jinn
 
 [dvc extension]:
   https://marketplace.visualstudio.com/items?itemName=Iterative.dvc
-[dvc studio]: https://studio.datachain.ai
 [live metrics and plots]: #live-metrics-and-plots
 [push]: #push-experiments
 [pull]: #pull-experiments
diff --git a/content/docs/user-guide/experiment-management/visualizing-plots.md b/content/docs/user-guide/experiment-management/visualizing-plots.md
@@ -276,7 +276,7 @@ view.
 
 <tab title="DVC Studio">
 
-Once you have [shared] the results to [DVC Studio], you can
+Once you have [shared] the results to DVC Studio, you can
 [compare experiments](/doc/studio/user-guide/experiments/visualize-and-compare)
 against the entire repo history:
 
@@ -288,4 +288,3 @@ against the entire repo history:
 
 [dvclive]: /doc/dvclive
 [shared]: /doc/user-guide/experiment-management/sharing-experiments
-[dvc studio]: https://studio.datachain.ai
diff --git a/content/docs/user-guide/integrations/databricks.md b/content/docs/user-guide/integrations/databricks.md
@@ -88,7 +88,7 @@ entries to corresponding `.gitignore`s, so you'll need to do that manually.
 
 If working with [Databricks Repos], you will need to set both the
 `DVC_STUDIO_TOKEN` and `DVC_EXP_GIT_REMOTE` to see [live experiment updates] in
-[DVC Studio].
+DVC Studio.
 
 ```python
 import getpass
@@ -107,4 +107,3 @@ os.environ["DVC_EXP_GIT_REMOTE"] = "https://github.com/<org>/<repo>"
 [web terminal]: https://docs.databricks.com/en/clusters/web-terminal.html
 [live experiment updates]:
   /doc/studio/user-guide/experiments/live-metrics-and-plots
-[DVC Studio]: https://studio.datachain.ai
diff --git a/content/docs/user-guide/integrations/sagemaker.md b/content/docs/user-guide/integrations/sagemaker.md
@@ -31,8 +31,7 @@ you would in any other environment. Take a look at DVC [experiments] for how to
 get started with DVC in notebooks (if you have setup [code-server] on SageMaker,
 you can also install the [DVC extension for VS Code]).
 
-If you would like to see live experiment updates in [DVC Studio], set your
-token:
+If you would like to see live experiment updates in DVC Studio, set your token:
 
 ```cli
 $ dvc studio login
@@ -128,7 +127,7 @@ The end result of running the pipeline looks like this:
 
 SageMaker jobs run outside of your Git repository, so experiment metrics and
 plots will not be automatically tracked in the repository. However, you can see
-[live experiment updates] in [DVC Studio].
+[live experiment updates] in DVC Studio.
 
 First, set the `DVC_STUDIO_TOKEN` and `DVC_EXP_GIT_REMOTE` environment
 variables.
@@ -185,7 +184,6 @@ For a full example of how to deploy with SageMaker, see our [blog post].
 [dvc extension for vs code]: /doc/vs-code-extension
 [live experiment updates]:
   /doc/studio/user-guide/experiments/live-metrics-and-plots
-[dvc studio]: https://studio.datachain.ai
 [dvc config]: /doc/user-guide/project-structure/configuration#studio
 [pipelines]: /doc/user-guide/pipelines
 [external dependencies and outputs]:
diff --git a/content/docs/user-guide/project-structure/configuration.md b/content/docs/user-guide/project-structure/configuration.md
diff --git a/gatsby-config.js b/gatsby-config.js
diff --git a/package.json b/package.json
diff --git a/src/components/Home/Alert/index.tsx b/src/components/Home/Alert/index.tsx
diff --git a/src/constants/externalUrls.ts b/src/constants/externalUrls.ts
diff --git a/yarn.lock b/yarn.lock
