Using dvc.yaml instead of artifacts.yaml

content/docs/dvclive/index.md

@@ -42,6 +42,15 @@ live.log_artifact("model.pt")
 
 See `Live.log_artifact()`.
 
+</tab>
+<tab title="Models">
+
+```python
+live.log_artifact("model.pt", type="model")
+```
+
+See `Live.log_artifact()`.
+
 </tab>
 <tab title="Images">
 

content/docs/dvclive/live/log_artifact.md

@@ -43,7 +43,7 @@ If `Live` was initialized with `dvcyaml=True` (which is the default), it will
 add an [artifact](/doc/user-guide/project-structure/dvcyaml-files#artifacts) and
 all the metadata passed as arguments to the corresponding `dvc.yaml`. Passing
 `type="model"` will mark it as a `model` for DVC and will make it appear in
-[Studio Model Registry](/doc/studio) (coming soon).
+[Studio Model Registry](/doc/studio).
 
 If `name` is not provided, the path stem (last part of the path without the file
 extension) will be used as the artifact name.

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

@@ -38,9 +38,13 @@ There are some examples below
 from dvclive.lightning import DVCLiveLogger
 
 ...
-
-trainer = Trainer(logger=DVCLiveLogger(save_dvc_exp=True))
-trainer.fit(model)
+with Live(save_dvc_exp=True) as live:
+    trainer = Trainer(
+        logger=DVCLiveLogger(save_dvc_exp=True),
+        default_root_dir="mymodel"
+    )
+    trainer.fit(model)
+    live.log_artifact("mymodel", type="model")
 ```
 
 </tab>
@@ -51,9 +55,11 @@ trainer.fit(model)
 from dvclive.huggingface import DVCLiveCallback
 
 ...
-
-trainer.add_callback(DVCLiveCallback(save_dvc_exp=True))
-trainer.train()
+with Live(save_dvc_exp=True) as live:
+    trainer.add_callback(DVCLiveCallback(save_dvc_exp=True))
+    trainer.train()
+    trainer.save_model("mymodel")
+    live.log_artifact("mymodel", type="model")
 ```
 
 </tab>
@@ -64,10 +70,14 @@ trainer.train()
 from dvclive.keras import DVCLiveCallback
 
 ...
-
-model.fit(
-  train_dataset, validation_data=validation_dataset,
-  callbacks=[DVCLiveCallback(save_dvc_exp=True)])
+with Live(save_dvc_exp=True) as live:
+    model.fit(
+        train_dataset,
+        validation_data=validation_dataset,
+        callbacks=[DVCLiveCallback(save_dvc_exp=True)]
+    )
+    model.save("mymodel")
+    live.log_artifact("mymodel", type="model")
 ```
 
 </tab>
@@ -86,6 +96,8 @@ with Live(save_dvc_exp=True) as live:
         for metric_name, value in metrics.items():
             live.log_metric(metric_name, value)
         live.next_step()
+
+    live.log_artifact("model.pkl", type="model")
 ```
 
 </tab>

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

@@ -2,9 +2,9 @@
 
 You can add models from any ML project to the model registry. To add a model to
 your model registry, Iterative Studio creates an annotation for it in an
-`artifacts.yaml` file in your Git repository. If you are using the [GTO] command
-line tool, you can also add models [from the CLI][gto annotate]. To add models
-using Iterative Studio, watch this tutorial video or read on below:
+`dvc.yaml` file in your Git repository. If you are using the [GTO] command line
+tool, you can also add models [from the CLI][gto annotate]. To add models using
+Iterative Studio, watch this tutorial video or read on below:
 
 https://www.youtube.com/watch?v=szzv4ZXmYAs
 
@@ -28,10 +28,8 @@ https://www.youtube.com/watch?v=szzv4ZXmYAs
      project path of the corresponding `.dvc` file.
    - If the model file is in remote storage and is not DVC-tracked, enter the
      absolute path of the model file.
-   - If you use [MLEM] to save your model, use the path to the binary file that
-     MLEM generates. After you have run
-     [`mlem init`](https://mlem.ai/doc/command-reference/init), Iterative Studio
-     will be able to parse the `.mlem` file to extract model metadata.
+   - If you use [MLEM] to save your model, use the path to the binary file or
+     folder that MLEM generates.
 
    If the path you entered is a cloud path, Iterative Studio will ask you for
    the repository path where the dvc reference to the model should be saved.
@@ -54,10 +52,10 @@ https://www.youtube.com/watch?v=szzv4ZXmYAs
 8. At this point, the new model appears in the models dashboard.
 
 9. In your Git repository, you will find that an entry for the new model has
-   been created in the `artifacts.yaml` file in the repository's root. If you
-   had committed to a new branch, a new pull request (or merge request in the
-   case of GitLab) will also have been created to merge the new branch into the
-   base branch.
+   been created in the `dvc.yaml` file in the repository's root. If you had
+   committed to a new branch, a new pull request (or merge request in the case
+   of GitLab) will also have been created to merge the new branch into the base
+   branch.
 10. If you had added a model from a cloud storage, the following will also
     happen before the commit is created:
 

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

@@ -54,7 +54,7 @@ Note that while you can get the basic Model Registry functionality within
 Iterative Studio, there are more things you can do using the [MLEM] and [GTO]
 command line interface (CLI). For example, to save and deploy models, you will
 need to use MLEM, although future iterations of the Model Registry may
-incorporate these tasks also. Similarly, you can use GTO in your CI/CD actions
+incorporate these tasks also. Similarly, you can use [GTO] in your CI/CD actions
 to interpret Git tags for deploying the models to the desired environment.
 
 [semantic versioning]: https://semver.org/

content/docs/user-guide/experiment-management/index.md

@@ -46,7 +46,14 @@ To save an experiment, you can follow one of these roads:
 Experiments are saved locally by default but you can [share] them so that anyone
 can reproduce your work.
 
-## Metrics, plots, and parameters
+## Datasets and models
+
+DVC can track datasets or models as part of your repo. One way to let DVC know
+the specific artifact is a model or a dataset is to use [DVCLive]. You can also
+manually add them to `dvc.yaml`. For models, you'll see them appear in [Studio
+Model Registry].
+
+## Metrics, plots, parameters
 
 DVC can track and compare <abbr>parameters</abbr>, <abbr>metrics</abbr>, and
 <abbr>plots</abbr> data saved in standard structured files like YAML, JSON, and
@@ -74,9 +81,12 @@ https://www.youtube.com/watch?v=LHi3SWGD9nc
 [pipeline]: /doc/user-guide/pipelines
 [run]: /doc/user-guide/experiment-management/running-experiments
 [share]: /doc/user-guide/experiment-management/sharing-experiments
+[artifacts]: /doc/user-guide/project-structure/dvcyaml-files#artifacts
 [parameters]: /doc/user-guide/project-structure/dvcyaml-files#params
 [metrics]: /doc/user-guide/project-structure/dvcyaml-files#metrics
 [plots]: /doc/user-guide/project-structure/dvcyaml-files#plots
 [visualize plots]: /doc/user-guide/experiment-management/visualizing-plots
 [from the vs code ide]: /doc/vs-code-extension
 [iterative studio]: /doc/studio
+[studio model registry]:
+  /doc/studio/user-guide/model-registry/what-is-a-model-registry

content/docs/user-guide/project-structure/dvcyaml-files.md

@@ -20,8 +20,7 @@ Although you can specify artifacts of any `type`, we are in the process of
 building a DVC-based [model registry](/doc/use-cases/model-registry) that will
 pick up any artifacts with type `model`. Additionally, they will be picked up
 and supported by
-[Studio Model Registry](/doc/studio/user-guide/model-registry/what-is-a-model-registry)
-(coming soon).
+[Studio Model Registry](/doc/studio/user-guide/model-registry/what-is-a-model-registry).
 
 ```yaml
 artifacts:
@@ -37,7 +36,7 @@ artifacts:
 ```
 
 Artifact IDs must consist of letters and numbers, and use '-' as separator (but
-not at the start or end). The first character must be a letter.
+not at the start or end).
 
 ## Metrics