Model Registry docs

content/docs/sidebar.json

@@ -521,46 +521,38 @@
     "icon": "vscode"
   },
   {
-    "label": "Studio",
     "slug": "studio",
-    "source": "studio/index.md",
     "icon": "studio",
     "children": [
-      {
-        "label": "How It Works",
-        "slug": "overview"
-      },
+      "how-it-works",
       "get-started",
       {
-        "label": "User Guide",
         "slug": "user-guide",
-        "source": "user-guide/index.md",
+        "source": false,
         "children": [
+          "prepare-your-repositories",
           {
-            "label": "Prepare Your Repositories",
-            "slug": "prepare-repositories"
-          },
-          {
-            "label": "Projects and Experiments",
-            "slug": "projects",
+            "slug": "projects-and-experiments",
             "source": false,
             "children": [
               "what-is-a-project",
-              "create-project",
-              {
-                "label": "Configure Project",
-                "slug": "project-settings"
-              },
-              {
-                "label": "Explore ML Experiments",
-                "slug": "explore-experiments"
-              },
-              {
-                "label": "Visualize and Compare",
-                "slug": "visualize-experiments"
-              },
+              "create-a-project",
+              "configure-a-project",
+              "explore-ml-experiments",
+              "visualize-and-compare",
               "run-experiments",
-              "share-project"
+              "share-a-project"
+            ]
+          },
+          {
+            "slug": "model-registry",
+            "source": false,
+            "children": [
+              "what-is-a-model-registry",
+              "add-a-model",
+              "view-models",
+              "version-models",
+              "promote-models"
             ]
           },
           "teams",

content/docs/studio/get-started.md

@@ -8,6 +8,8 @@ Studio.
 
 https://www.youtube.com/watch?v=hKf4twg832g
 
+## Sign up / sign in
+
 1. Sign in to your [Iterative Studio](https://studio.iterative.ai/) dashboard
    using your GitHub.com, GitLab.com or Bitbucket.org account, or your email
    address.
@@ -25,20 +27,24 @@ different plans, check out our
 
 </admon>
 
-2. If this is the first time you are signing in to Iterative Studio, you will
+## Explore demo project
+
+1. If this is the first time you are signing in to Iterative Studio, you will
    see that there already exists a `Demo` project that connects to an example
-   DVC repository. Use this project to explore the features that Iterative
-   Studio has to offer.
+   repository. Use this project to explore the features that Iterative Studio
+   has to offer.
 
 ![](https://static.iterative.ai/img/studio/login_home_v3.png) _When you first
 login, an example project is already created for you to explore, and you can add
 more projects._
 
-3. Each project on this dashboard displays the metrics that Iterative Studio
+2. Each project on this dashboard displays the metrics that Iterative Studio
    identified in your Git repository. In the figure above, you can see that
    `avg_prec` and `roc_auc` metrics are displayed.
 
-4. To connect to a new repository, click on `Add a Project`. All the
+## Add your projects
+
+1. To connect to a new repository, click on `Add a Project`. All the
    organizations that you have access to will be listed.
 
     <admon type="info">
@@ -59,47 +65,80 @@ more projects._
 
     </admon>
 
-5. Open the organization whose repository you want to connect to. You can also
+2. Open the organization whose repository you want to connect to. You can also
    use the search bar to directly look for a repository.
 
    ![](https://static.iterative.ai/img/studio/select_repo_v3.png)
 
-6. Specify additional connection settings if required.
+3. Specify additional connection settings if required.
 
    ![](https://static.iterative.ai/img/studio/project_settings.png)
 
 > Project settings must be configured if you are connecting to a non-DVC
 > repository, if your metrics are in some custom files, if you are connecting to
 > a monorepo, or if your metrics are in cloud or other remote storage. However,
-> you can configure the project settings after the project has been created. So,
-> you can `Skip and Continue` now. Refer to the
-> [Project Settings](/doc/studio/user-guide/projects/project-settings) section
-> for more details.
+> you can configure the project settings after the project has been created. So
+> you can `Skip and Continue` now. Refer to the [Project Settings] section for
+> more details.
 
 You should now see that a project has been added in your dashboard.
 
-7. If your project requires any of the additional settings, then remember to
-   configure them by opening the
-   [project settings](/doc/studio/user-guide/projects/project-settings).
-   Otherwise, your project may not work as expected. To go to project settings,
-   click on the
+4. If your project requires any of the additional settings, then remember to
+   configure them by opening the [project settings]. Otherwise, your project may
+   not work as expected. To go to project settings, click on the
    ![](https://static.iterative.ai/img/studio/view_open_settings_icon.png) icon
    in the project. In the menu that opens up, click on `Settings`.
 
 ![](https://static.iterative.ai/img/studio/project_open_settings.png)
 
-8. You can also
-   [create multiple projects from a single Git repository](/doc/studio/user-guide/projects/create-project#create-multiple-projects-from-a-single-git-repository).
+5. You can also [create multiple projects from a single Git repository].
+
+## Explore and share your projects
+
+1. After creating a project, you can use it to [explore the ML experiments] as
+   well as [submit new experiments] from Iterative Studio.
+
+2. You can also make the project public by [sharing it on the web].
+
+## Use model registry
+
+1. Click on the `Models` tab to open the central
+   [Models dashboard](/doc/studio/user-guide/model-registry/view-models#models-dashboard).
+   If any of your projects contain [GTO-annotated models], they will be listed
+   here automatically.
+
+   [gto-annotated models]: /doc/studio/user-guide/model-registry/add-a-model
+
+2. You can [add new models](/doc/studio/user-guide/model-registry/add-a-model)
+   to the model registry by clicking on the `Add a model` button and following
+   on-screen instructions.
+
+3. To
+   [view the details](/doc/studio/user-guide/model-registry/view-models#model-details-page)
+   of a model, click on the name of the model.
+
+4. You can
+   [register new versions](/doc/studio/user-guide/model-registry/version-models)
+   of the models and
+   [promote them to different stages](/doc/studio/user-guide/model-registry/promote-models)
+   (e.g., development, staging, production).
 
-9. After creating a project, you can use it to
-   [explore the ML experiments](/doc/studio/user-guide/explore-experiments) as
-   well as
-   [submit new experiments from Iterative Studio](/doc/studio/user-guide/run-experiments).
+## Create and manage teams
 
-10. You can also make the project public by
-    [sharing it on the web](/doc/studio/user-guide/projects/share-project).
+1. You can [create a team] and invite collaborators. Each team will have its own
+   projects dashboard. To create teams with more than 2 team members, [sign up
+   for the **Team** plan or **Enterprise** plan].
 
-11. You can [create a team](/doc/studio/user-guide/teams) and invite
-    collaborators. Each team will have its own projects dashboard. To create
-    teams with more than 2 team members,
-    [sign up for the **Team** plan or **Enterprise** plan](/doc/studio/user-guide/change-team-plan-and-size).
+[project settings]:
+  /doc/studio/user-guide/projects-and-experiments/configure-a-project
+[create multiple projects from a single git repository]:
+  /doc/studio/user-guide/projects-and-experiments/create-a-project#create-multiple-projects-from-a-single-git-repository
+[explore the ml experiments]:
+  /doc/studio/user-guide/projects-and-experiments/explore-ml-experiments
+[submit new experiments]:
+  /doc/studio/user-guide/projects-and-experiments/run-experiments
+[sharing it on the web]:
+  /doc/studio/user-guide/projects-and-experiments/share-a-project
+[create a team]: /doc/studio/user-guide/teams
+[sign up for the **team** plan or **enterprise** plan]:
+  /doc/studio/user-guide/change-team-plan-and-size

content/docs/studio/how-it-works.md

@@ -0,0 +1,66 @@
+# How Iterative Studio Works
+
+[Iterative Studio](https://studio.iterative.ai/) works with the data, metrics,
+hyperparameters and model metadata that you add to your ML project Git
+repositories. It works very closely with your Git ecosystem.
+
+This video illustrates how Iterative Studio works closely with your Git
+ecosystem.
+
+> Note that we have renamed DVC Studio to Iterative Studio and Views to
+> Projects.
+
+https://www.youtube.com/watch?v=5xM5az78Lrg
+
+## How your project data is saved
+
+- Using [DVC] and [Git], you will push all your ML experiments to your GitHub,
+  GitLab or Bitbucket repositories as Git commits.
+- Using Iterative Studio, or using the command line interface (CLI) of [GTO],
+  and possibly [MLEM], you will push all your ML model details to the Git
+  repositories as Git commits and Git tags.
+
+## How Iterative Studio extracts your project data
+
+- When you connect to these Git repositories from Iterative Studio, the
+  project's `dvc.yaml` is used to identify all the data, metrics and
+  hyperparameters in your experiments.
+- If you are not using DVC, you can
+  [add the metrics and hyperparameters to your Git repositories manually](/doc/studio/user-guide/projects-and-experiments/configure-a-project#custom-metrics-and-parameters).
+- Details of your ML models, including versions and stage assignments, are
+  extracted from the Git commits and tags.
+
+## How Iterative Studio presents your project data
+
+- Iterative Studio creates an
+  [interactive, tabular representation](/doc/studio/user-guide/projects-and-experiments/explore-ml-experiments#components-of-a-project)
+  of all the identified values.
+- All the projects that you have created are presented in a central projects
+  dashboard.
+- All identified models are included in an interactive
+  [models dashboard](/doc/studio/user-guide/model-registry/view-models#models-dashboard)
+  and individual
+  [model details pages](/doc/studio/user-guide/model-registry/view-models#model-details-page).
+
+## How Iterative Studio saves updates to your ML projects
+
+- When you
+  [run new experiments](/doc/studio/user-guide/projects-and-experiments/run-experiments)
+  or
+  [add models to your model registry](/doc/studio/user-guide/model-registry/add-a-model)
+  in Iterative Studio, it creates Git commits and pull requests with the
+  changes.
+- You can set up your CI/CD actions (e.g. GitHub Actions) to run model training
+  upon the creation of Git commits, tags or pull requests. You can use [CML] in
+  your CI/CD actions for continuous machine learning.
+- When you
+  [register new versions](/doc/studio/user-guide/model-registry/version-models)
+  of your ML models or
+  [assign stages](/doc/studio/user-guide/model-registry/promote-models) to them,
+  Iterative Studio creates annotated Git tags representing the actions.
+
+[dvc]: https://dvc.org/
+[cml]: https://cml.dev
+[mlem]: https://mlem.ai/
+[gto]: https://github.com/iterative/gto
+[git]: https://git-scm.com/

content/docs/studio/index.md

@@ -1,15 +1,15 @@
 # Iterative Studio
 
-[`Iterative Studio`](https://studio.iterative.ai/) is a web application that you
-can [access online](https://studio.iterative.ai/) or even host on-prem. It works
-with the data, metrics and hyperparameters that you add to your ML project
-repositories. Using the power of leading open-source tools DVC,
-[CML](https://cml.dev) and Git, it enables you to seamlessly manage data and
-models, run and track experiments, and visualize and share results.
+[Iterative Studio](https://studio.iterative.ai/) is a web application that you
+can access online or even host on-prem. Using the power of leading open-source
+tools [DVC](https://dvc.org/), [CML](https://cml.dev), [MLEM](https://mlem.ai/),
+[GTO](https://github.com/iterative/gto) and [Git](https://git-scm.com/), it
+enables you to seamlessly manage data and machine learning models, run and track
+experiments, and visualize and share results.
 
 <cards>
 
-  <card href="/doc/studio/overview" heading="How Iterative Studio works">
+  <card href="/doc/studio/how-it-works" heading="How Iterative Studio works">
     Learn how Iterative Studio works closely with the Git ecosystem
   </card>
 
@@ -35,3 +35,7 @@ models, run and track experiments, and visualize and share results.
 - Apply your existing software engineering stack for ML teams.
 - Automate your ML process by transitioning to a no-code interface for running
   experiments on cloud resources of your choice.
+- Build an ML model registry for transparency and discovery across all your
+  projects.
+- Because your model registry is built on top of Git, you can use GitOps for
+  model deployment.