Few model registry docs updates

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

@@ -44,7 +44,7 @@ with Live(save_dvc_exp=True) as live:
         Path("model.pt").write_text(str(random.random()))
         live.next_step()
 
-    live.log_artifact("model.pt")
+    live.log_artifact("model.pt", type="model", name="mymodel")
     live.log_sklearn_plot("confusion_matrix", [0, 0, 1, 1], [0, 1, 0, 1])
     live.log_metric("summary_metric", 1.0, plot=False)
 # live.end() has been called at this point
@@ -92,8 +92,8 @@ the versioned artifact from the Git commit.
 If `Live` was initialized with `dvcyaml=True` (which is the default), this will
 add an [artifact](/doc/user-guide/project-structure/dvcyaml-files#artifacts) to
 the corresponding `dvc.yaml`. Passing `type="model"` will mark it as a `model`
-for DVC and will also make [Studio Model Registry](/doc/studio) support it
-(coming soon!).
+for DVC and will also show it in
+[Studio Model Registry](/doc/studio/user-guide/model-registry/what-is-a-model-registry).
 
 ### Run with DVC
 

content/docs/dvclive/index.md

@@ -145,6 +145,8 @@ with Live(save_dvc_exp=True) as live:
             live.log_metric(metric_name, value)
 
         live.next_step()
+
+    live.log_artifact(path, type="model", name=name)
 ```
 
 ## Outputs

content/docs/studio/get-started.md

@@ -75,18 +75,25 @@ details]).
    assignments.
 
    [models dashboard]:
-     /doc/studio/user-guide/model-registry/view-models#models-dashboard
+     /doc/studio/user-guide/model-registry/view-and-compare-models#models-dashboard
 
 2. Click on the model name to
-   [open its details page](/doc/studio/user-guide/model-registry/view-models#model-details-page).
-
-3. You can perform the following actions to manage the life cycle of models:
-   - [Register new models](/doc/studio/user-guide/model-registry/add-a-model)
-     from your Git repositories and remote (cloud) storages.
-   - [Register model versions](/doc/studio/user-guide/model-registry/register-version)
-   - [Assign stages](/doc/studio/user-guide/model-registry/assign-stage) (e.g.,
-     development, staging, production)
-   - Unassign stages, deregister versions or deprecate (remove) models
+   [open its details page](/doc/studio/user-guide/model-registry/view-and-compare-models#model-details-page).
+
+3. You can perform the following actions to manage the lifecycle of models:
+   - [Add models to the registry](/doc/studio/user-guide/model-registry/add-a-model)
+   - [Register new versions of the models](/doc/studio/user-guide/model-registry/register-version)
+     (like `v0.0.1` and `v1.0.0`)
+   - [Assign stages to model versions](/doc/studio/user-guide/model-registry/assign-stage)
+     (e.g. `dev`, `testing`, `prod`)
+   - Use models:
+     [download them](/doc/studio/user-guide/model-registry/use-models) or
+     [set up CI/CD](/doc/studio/user-guide/model-registry/use-models) to publish
+     or deploy models
+
+Check out
+[`What is a model registry?`](/doc/studio/user-guide/model-registry/what-is-a-model-registry)
+to get started.
 
 ## Collaborate
 

content/docs/studio/user-guide/model-registry/add-a-model.md

@@ -5,9 +5,9 @@ your model registry, Iterative Studio creates an annotation for it in a
 `dvc.yaml` file in your Git repository. You can add a model in any of the
 following ways:
 
-1. Log your model during the training process using [dvclive].
-2. Edit [`dvc.yaml`](/doc/user-guide/project-structure/dvcyaml-files#artifacts)
-   directly.
+1. Log your model during the training process using [dvclive] by calling
+   [`live.log_artifact(path, type="model")`][log_artifact] method.
+2. Edit `dvc.yaml` directly and add your model to [`artifacts`] section.
 3. Use the Iterative Studio interface (watch this tutorial video or read on
    below).
 
@@ -41,8 +41,7 @@ https://www.youtube.com/watch?v=szzv4ZXmYAs
 
 5. Provide labels for your model. For example, if your model is about reviewing
    sentiment analysis using natural language processing, one of the labels may
-   be `nlp`. You can provide multiple labels as a comma separated list. Eg,
-   `nlp, sentiment_analysis`.
+   be `nlp` or `sentiment_analysis`.
 
 6. Optionally, add a brief description for your model.
 
@@ -75,4 +74,6 @@ before the commit is created:
   /doc/studio/user-guide/projects-and-experiments/create-a-project
 [gto]: https://mlem.ai/doc/gto
 [mlem]: https://mlem.ai/
-[dvclive]: /doc/dvclive/live/log_artifact
+[dvclive]: /doc/dvclive
+[log_artifact]: /doc/dvclive/live/log_artifact
+[`artifacts`]: /doc/user-guide/project-structure/dvcyaml-files#artifacts

content/docs/studio/user-guide/model-registry/use-models.md

@@ -73,3 +73,7 @@ for you to use them in CI.
 That example repo also introduces a workflow
 [that deploys a model](https://github.com/iterative/example-gto/blob/mlem/.github/workflows/deploy-model-with-mlem.yml)
 from CI/CD using MLEM.
+
+Finally, you can find examples of building a Docker Image with a model and
+deploying it to the cloud in
+[GTO User Guide](https://mlem.ai/doc/gto/user-guide#acting-on-new-registrations-and-assignments).