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content/docs/studio/experiments.md

@@ -15,7 +15,8 @@ terminal, etc. To quickly start tracking your experiments with Iterative Studio:
 
 - Copy your
   [DVC Studio token](/doc/studio/user-guice/account-and-billing#studio-access-token)
-  and configure your model training environment to use the token:
+  and
+  [configure your model training environment to use the token](/doc/studio/user-guide/projects-and-experiments/live-metrics-and-plots#set-up-an-access-token):
 
   ```cli
   dvc config --global studio.token ***

content/docs/studio/model-registry.md

@@ -1,9 +1,9 @@
 # Manage models
 
 Iterative Studio provides a model registry that you can access by clicking on
-the `Models` tab. Iterative Studio uses your project's `dvc.yaml` files to
-identify ML models and specially formatted Git tags to identify model versions
-and stage assignments.
+the `Models` tab. Your project's `dvc.yaml` files are used to identify ML models
+and specially formatted Git tags are used to identify model versions and stage
+assignments.
 
 To quickly start tracking your models in the Iterative Studio model registry:
 

content/docs/studio/user-guide/experiments/index.md

@@ -1,72 +1,36 @@
 # Experiment tracking and management
 
 You can submit your experiments from your favorite interface - whether it is
-Jupyter Notebooks, a code editor or IDE like VS Code, the Python cli, the bash
-terminal, etc.
+Jupyter Notebooks, a code editor or IDE like [VS Code](/doc/vs-code-extension),
+the Python cli, the bash terminal, etc. You can also [submit new experiments
+from Iterative Studio][run-experiments].
 
-To track and manage experiments,
-[create a project](/doc/studio/uer-guide/experiments/create-a-project) in
-Iterative Studio. A project is an interactive representation of the ML datasets,
-hyperparameters, models and metrics defined in your Git repositories. These
-values are configured in your project's `dvc.yaml` file. Additionally, live
-metrics that you [send to Iterative Studio][live-metrics-and-plots] using
-[DVCLive] are also included in the project.
-
-Within a project, you can:
-
-- [Explore all the details of the experiments][explore-ml-experiments] that you
-  have pushed to your Git repository.
-- [Visualize the experiments][visualize] using plots and trend charts.
-- [Compare experiments][compare].
-- [Run new experiments][run-experiments], including on your own cloud instances,
-  directly from Iterative Studio.
-- [Track live metrics and plots][live-metrics-and-plots] by sending them to
-  Iterative Studio by using [DVCLive].
-
-[project settings]:
-  /doc/studio/user-guide/projects-and-experiments/configure-a-project
-[when do you need project settings]:
-  /doc/studio/user-guide/projects-and-experiments/configure-a-project#scenarios-where-project-settings-are-required
-[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 ml experiments]:
-  /doc/studio/user-guide/projects-and-experiments/explore-ml-experiments
-[create a team]: /doc/studio/user-guide/teams
-[sign up for the **basic** or **enterprise** plan]:
-  /doc/studio/user-guide/change-team-plan-and-size
-[make your projects public]:
-  /doc/studio/user-guide/projects-and-experiments/share-a-project
-[train on the cloud, including on your own cloud infrastructure, and submit new experiments]:
-  /doc/studio/user-guide/projects-and-experiments/run-experiments
-[live-metrics-and-plots]:
-  /doc/studio/user-guide/projects-and-experiments/live-metrics-and-plots
-[dvclive]: /doc/dvclive
-[monorepo]:
-  /doc/studio/user-guide/projects-and-experiments/configure-a-project#monorepo
+To quickly start tracking your experiments with Iterative Studio, check out the
+[experiments quick start guide](/doc/studio/experiments).
 
 ## Different ways to track experiments
 
-Iterative Studio offers a few different ways to track your experiments.
-
-We recommend combining all these methods in a single project, so that you can
-easily track live and completed experiments.
+You can track live as well as completed experiments in Iterative Studio. First,
+click on `Add a project` to connect Iterative Studio to your ML project's Git
+repository. Then, follow the instructions presented below.
 
 ### 1. To track experiments in real-time, use [DVCLive]:
 
-- Install [DVCLive]:
+- In your model training environment, install [DVCLive]:
 
   ```cli
   pip install dvclive
   ```
 
-- Copy your DVC Studio token and configure your model training environment to
-  use the token:
+- Copy your
+  [DVC Studio token](/doc/studio/user-guice/account-and-billing#studio-access-token)
+  and configure your model training environment to use the token:
 
   ```cli
   dvc config --global studio.token ***
   ```
 
-- Add DVCLive to your model training code:
+- Use the DVCLive log_metric() method in your model training code:
 
   ```python
   from dvclive import Live
@@ -85,12 +49,12 @@ easily track live and completed experiments.
   python train.py
   ```
 
-- The metrics and plots will be updated in the project in real-time. [Learn
-  more][live-metrics-and-plots]
+- The metrics and plots will be [tracked live][live-metrics-and-plots] in the
+  project in Iterative Studio.
 
-### 2. To track outputs of reproducible pipelines, [initialize DVC](https://dvc.org/doc):
+### 2. To set up, run and track [reproducible pipelines](/doc/start/experiments/experiment-pipelines), use [DVC](https://dvc.org/doc):
 
-- Install [DVC]:
+- In your model training environment, install [DVC]:
 
   ```cli
   pip install dvc
@@ -104,31 +68,53 @@ easily track live and completed experiments.
 
   This will create a `dvc.yaml` file in the Git repository.
 
-- When running your experiments, save your metrics, plots, models and other
-  files to `dvc.yaml`. You can automate this by [setting up DVC pipelines].
+- Copy your
+  [DVC Studio token](/doc/studio/user-guice/account-and-billing#studio-access-token)
+  and
+  [configure your model training environment to use the token](/doc/studio/user-guide/projects-and-experiments/live-metrics-and-plots#set-up-an-access-token):
+
+  ```cli
+  dvc config --global studio.token ***
+  ```
+
+- [Set up the DVC
+  pipeline]/doc/start/experiments/experiment-pipelines#creating-the-experiment-pipeline
+  for model training.
+
+- [Run an experiment](/doc/start/experiments/experiment-pipelines#modifying-parameters):
 
-- After the experiment completes, commit and push the `dvc.yaml` file to your
-  Git remote.
+  ```cli
+  dvc exp run
+  ```
+
+  Metrics, plots and other details of your experiment get saved in your
+  project's `dvc.yaml` file.
 
-- Studio reads the values of metrics, plots, models and other details from this
-  `dvc.yaml`.
+- When the experiment completes,
+  [push the results](/doc/start/experiments/experiment-collaboration#sharing):
+
+  ```cli
+  dvc exp push origin <exp_name>
+  ```
 
-### 3. To avoid over-crowding your Git history, use `dvc exp push`
+- Iterative Studio reads the values of metrics, plots and other details from
+  your project's `dvc.yaml` file, and displays them in an experiment row in the
+  project table. The experiment row will appear nested within the parent Git
+  commit row.
 
-- Once you have initialized DVC, you can also use the `dvc exp push` command to
-  **push experiments without creating separate Git commits** for them.
+- From here, you can
+  [persist the experiments](/doc/start/experiments/experiment-collaboration#persisting)
+  as Git commits and PRs as well as
+  [remove unnecessary experiments](/doc/start/experiments/experiment-collaboration#removing).
 
-- To notify Iterative Studio when you push experiments using `dvc exp push`,
-  [configure the `DVC_STUDIO_TOKEN` environment variable](/doc/studio/user-guide/projects-and-experiments/live-metrics-and-plots#set-up-an-access-token).
+<admon type="tip">
 
 If you are working with a **non-DVC repository**, you can
 [indicate which files contain metrics and hyperparameters](/doc/studio/user-guide/projects-and-experiments/configure-a-project#custom-metrics-and-parameters)
 that Iterative Studio should display in the project. However, we strongly
 recommend using DVC to avail of all the features of Iterative Studio.
 
-To **run new experiments** from Iterative Studio, add credentials with
-sufficient access to your cloud. Optionally, set up Continuous Integration (CI)
-to run experiments automatically on CI triggers. [Learn more][run-experiments]
+</admon>
 
 [on project settings]:
   /doc/studio/user-guide/projects-and-experiments/configure-a-project#non-dvc-repositories
@@ -150,3 +136,37 @@ to run experiments automatically on CI triggers. [Learn more][run-experiments]
 [create data registries]: /doc/use-cases/data-registry
 [create data pipelines]: /doc/start/data-management/data-pipelines
 [ci/cd in machine learning]: /doc/use-cases/ci-cd-for-machine-learning
+
+## Visualize, compare and run experiments
+
+Within a project, you can:
+
+- [Explore all the details of the experiments][explore-ml-experiments] that you
+  have pushed to your Git repository.
+- [Visualize the experiments][visualize] using plots and trend charts.
+- [Compare experiments][compare].
+- [Run new experiments][run-experiments], including on your own cloud instances,
+  directly from Iterative Studio.
+- [Track live metrics and plots][live-metrics-and-plots] by sending them to
+  Iterative Studio by using [DVCLive].
+
+[project settings]:
+  /doc/studio/user-guide/projects-and-experiments/configure-a-project
+[when do you need project settings]:
+  /doc/studio/user-guide/projects-and-experiments/configure-a-project#scenarios-where-project-settings-are-required
+[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 ml experiments]:
+  /doc/studio/user-guide/projects-and-experiments/explore-ml-experiments
+[create a team]: /doc/studio/user-guide/teams
+[sign up for the **basic** or **enterprise** plan]:
+  /doc/studio/user-guide/change-team-plan-and-size
+[make your projects public]:
+  /doc/studio/user-guide/projects-and-experiments/share-a-project
+[train on the cloud, including on your own cloud infrastructure, and submit new experiments]:
+  /doc/studio/user-guide/projects-and-experiments/run-experiments
+[live-metrics-and-plots]:
+  /doc/studio/user-guide/projects-and-experiments/live-metrics-and-plots
+[dvclive]: /doc/dvclive
+[monorepo]:
+  /doc/studio/user-guide/projects-and-experiments/configure-a-project#monorepo