Model registry docs consolidation

content/basic-concepts/model_registry.md

@@ -5,5 +5,5 @@ tooltip: >-
   The model registry connects all of your team's models through Git. Find,
   organize, and manage all of them, and use GitOps workflows to version,
   promote, demote, and deploy them. [Learn
-  more](/doc/studio/user-guide/model-registry/what-is-a-model-registry).
+  more](/doc/studio/user-guide/model-registry).
 ---

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

@@ -101,8 +101,8 @@ make Git ignore it. It will generate a `model.pt.dvc` metadata file, which can
 be tracked in Git and becomes part of the experiment. With this metadata file,
 you can [retrieve](/doc/start/data-management/data-versioning#retrieving) the
 versioned artifact from the Git commit. You can also use
-`Live.log_artifact("model.pt", type="model")` to add it to the
-[DVC Studio Model Registry](/doc/studio/model-registry).
+`Live.log_artifact("model.pt", type="model")` to add it to the <abbr>model
+registry</abbr>.
 
 Using `Live.log_image()` to log multiple images may also grow too large to track
 with Git, in which case you can use

content/docs/gto/user-guide/index.md

@@ -3,10 +3,10 @@
 GTO lets you build an artifact registry or <abbr>model registry</abbr> out of
 your Git repository by creating annotated
 [Git tags](https://git-scm.com/book/en/v2/Git-Basics-Tagging) with a
-[special format](#git-tags-format). To read more about building a Model
-Registry, read this [DVC Studio User Guide].
+[special format](#git-tags-format). To read more about building a model
+registry, read this [get started guide].
 
-[DVC Studio User Guide]: https://dvc.org/doc/studio/user-guide/model-registry
+[get started guide]: /doc/start/model-registry
 
 ## Finding the right artifact version
 

content/docs/sidebar.json

@@ -68,16 +68,20 @@
         ]
       },
       {
-        "label": "Model Management",
-        "slug": "model-management",
-        "source": "model-management/index.md",
+        "label": "Model Registry",
+        "slug": "model-registry",
+        "source": "model-registry/index.md",
         "children": [
           {
-            "label": "Model registry",
-            "slug": "model-registry"
+            "label": "Add a model",
+            "slug": "add-model"
           },
           {
-            "label": "Using and deploying models",
+            "label": "Manage models",
+            "slug": "manage-models"
+          },
+          {
+            "label": "Use and deploy models",
             "slug": "model-cicd"
           }
         ]
@@ -716,6 +720,7 @@
           },
           {
             "slug": "model-registry",
+            "source": false,
             "children": [
               "add-a-model",
               "register-version",

content/docs/start/experiments/experiment-tracking.md

@@ -117,7 +117,7 @@ Framework and any
 also [log additional info](/doc/dvclive#log-data) to be included in the
 experiment. `live.log_artifact("mymodel", type="model")` will
 [track your model with DVC](/doc/dvclive/live/log_artifact) and enable managing
-it with [DVC Studio Model Registry](/doc/studio/user-guide/model-registry).
+it with the <abbr>model registry</abbr>.
 
 <admon type="info">
 

content/docs/start/index.md

@@ -77,13 +77,13 @@ Choose a trail to jump into its first chapter:
   by only instrumenting your code, and collaborate on ML experiments like
   software engineers do for code.
 
-- **[Model Management]** - Use the DVC model registry to manage the lifecycle of
+- **[Model Registry]** - Use the DVC model registry to manage the lifecycle of
   your models in an auditable way. Easily access your models and integrate your
   model registry actions into CICD pipelines to follow GitOps best practices.
 
 [Data Management]: /doc/start/data-management/data-versioning
 [Experiment Management]: /doc/start/experiments/experiment-tracking
-[Model Management]: /doc/start/model-management/model-registry
+[Model Registry]: /doc/start/model-registry/model-registry
 
 <admon type="tip">
 

content/docs/start/model-registry/add-model.md

@@ -0,0 +1,98 @@
+---
+title: 'Get Started: Add a model'
+description: 'Start tracking model artifacts with DVC.'
+---
+
+# Get Started: Add a model
+
+Just as we use experiment tracking to manage model development, it is a good
+idea to keep a <abbr>model registry</abbr> to manage the lifecycle of the models
+we get from our experiments. With DVC, we can start tracking models using our
+Git repository, which will serve as the single source of truth for our model
+registry and enable us to trigger automated CICD workflows.
+[DVC Studio](/doc/studio) will then provide a model registry on top of the Git
+repositories to
+[manage all our models](/doc/start/model-registry/manage-models).
+
+## Log a model artifact
+
+All you need to start is a <abbr>DVC repository</abbr> and the
+[DVCLive](/doc/dvclive) Python library installed:
+
+```cli
+$ pip install dvclive
+```
+
+Use the [`log_artifact`](/doc/dvclive/live/log_artifact) method to
+<abbr>cache</abbr> the model with DVC and add it to the model registry (this
+snippet is from the
+[training script](https://github.com/iterative/example-get-started-experiments/blob/main/src/train.py)
+in our example repository):
+
+<admon type="info">
+
+You can
+[fork our example repository](https://github.com/iterative/example-get-started-experiments/fork)
+and follow the
+[installation steps](https://github.com/iterative/example-get-started-experiments#installation)
+to set it up locally.
+
+</admon>
+
+```python
+from dvclive import Live
+
+
+with Live() as live:
+
+...
+
+    live.log_artifact(
+        str("models/model.pkl"),
+        type="model",
+        name="pool-segmentation",
+        desc="This is a Computer Vision (CV) model that's segmenting out swimming pools from satellite images.",
+        labels=["cv", "segmentation", "satellite-images", params.train.arch],
+    )
+```
+
+<details id="push-click-to-see-how-artifacts-are-registered">
+
+#### 💡 Expand to see how it works under the hood and other ways to add models
+
+When we call the `log_artifact()` method, DVC takes all the information we
+provide in the call and edits the `dvc.yaml` file which will now contain the
+following lines:
+
+```yaml
+artifacts:
+  pool-segmentation:
+    path: models/model.pkl
+    type: model
+    desc:
+      This is a Computer Vision (CV) model that's segmenting out swimming pools
+      from satellite images.
+    labels:
+      - cv
+      - segmentation
+      - satellite-images
+```
+
+If you don't want to add the model from a Python script, you can manually edit
+`dvc.yaml` files to add information about model artifacts.
+
+</details>
+
+## From model to registry
+
+If you are building your own repository, you will need to:
+
+1. Run the script
+2. Commit it and the resulting `dvc.yaml` file to Git
+3. Push the result to your Git remote (e.g., GitHub)
+
+If you are
+[following](/doc/start/model-registry/manage-models#follow-along-instructions)
+our example repository then this has already been done and we can continue to
+the next section to find out how to set up a <abbr>model registry</abbr> for all
+your models.

content/docs/start/model-registry/index.md

@@ -0,0 +1,49 @@
+---
+title: 'Get Started: Model Registry'
+description:
+  'Get started with the DVC model registry to manage the lifecycle of your
+  models in an auditable way. Easily access your models and integrate your model
+  registry actions into CICD pipelines to follow GitOps best practices.'
+---
+
+# Get Started: Model Registry
+
+https://www.youtube.com/watch?v=T7MBFpnSr9Q
+
+## Chapters
+
+- **[Add a model]** - Start tracking model artifacts with DVC.
+
+- **[Manage models in a central registry]** - Manage models, their versions and
+  lifecycle stages in a git-based model registry.
+
+- **[Use and deploy models]** - Easily download your models from the model
+  registry. Set up your CICD pipelines to be trigger by model registry actions
+  (such as assigning model stages) and deploy models directly form the model
+  registry.
+
+[Add a model]: /doc/start/model-registry/add-model
+[Manage models in a central registry]: /doc/start/model-registry/manage-models
+[Use and deploy models]: /doc/start/model-registry/model-cicd
+
+<admon type="info">
+
+These are captured in our [example-get-started-experiments] repo. You can [fork
+it][example-get-started-experiments-fork] to follow along, or you can take a
+look at our
+[public model registry](https://studio.iterative.ai/team/Iterative/models) (read
+only) to get a feel for what's possible.
+
+[example-get-started-experiments]:
+  https://github.com/iterative/example-get-started-experiments
+[example-get-started-experiments-fork]:
+  https://github.com/iterative/example-get-started-experiments/fork
+
+</admon>
+
+## Where To Go Next
+
+Pick a page from the list above, the left-side navigation bar, or just click
+`NEXT` below!
+
+Click [here](/doc/start/) to jump back to the Get Started landing page.