Updates Artifacts Landing Page

docs/guides/artifacts/intro.md

@@ -12,47 +12,60 @@ import { CTAButtons } from '@site/src/components/CTAButtons/CTAButtons.tsx';
 
 <CTAButtons productLink="https://wandb.ai/wandb/arttest/artifacts/model/iv3_trained/5334ab69740f9dda4fed/lineage" colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-artifacts/Pipeline_Versioning_with_W%26B_Artifacts.ipynb"/>
 
-Use W&B Artifacts to track and version any serialized data as the inputs and outputs of your [W&B Runs](../runs/intro.md). For example, a model training run might take in a dataset as input and trained model as output. In addition to logging hyper-parameters and metadata to a run, you can use an artifact to log the dataset used to train the model as input and the resulting model checkpoints as outputs. You will always be able answer the question “what version of my dataset was this model trained on”.
+Use W&B Artifacts to track and version data as the inputs and outputs of your [W&B Runs](../runs/intro.md). For example, a model training run might take in a dataset as input and produce a trained model as output. In addition to logging hyperparameters, metadata and metrics to a run, you can use an artifact to log, track and version the dataset used to train the model as input and another artifact for the resulting model checkpoints as outputs.
 
-In summary, with W&B Artifacts, you can:
-* [View where a model came from, including data it was trained on](./explore-and-traverse-an-artifact-graph.md).
-* [Version every dataset change or model checkpoint](./create-a-new-artifact-version.md).
-* [Easily reuse models and datasets across your team](./download-and-use-an-artifact.md).
+## Use cases
+You can use artifacts throughout your entire ML workflow as inputs and outputs of [runs](../runs/intro.md). You can use datasets, models, or even other artifacts as inputs for processing.
 
 ![](/images/artifacts/artifacts_landing_page2.png)
 
+| Use Case               | Input                       | Output                       |
+|------------------------|-----------------------------|------------------------------|
+| Model Training         | Dataset (training and validation data)     | Trained [Model](../models.md)                |
+| Dataset Pre-Processing | Dataset (raw data)          | Dataset (pre-processed data) |
+| Model Evaluation       | Model + Dataset (test data) | [W&B Table](../tables/intro.md)                        |
+| Model Optimization     | Model                       | Optimized Model              |
 
-The diagram above demonstrates how you can use artifacts throughout your entire ML workflow; as inputs and outputs of [runs](../runs/intro.md). 
 
-## How it works
+## Create an artifact
 
 Create an artifact with four lines of code:
-1. Create a [W&B run](../runs/intro.md).
+1. Create a [W&B Run](../runs/intro.md).
 2. Create an artifact object with the [`wandb.Artifact`](../../ref/python/artifact.md) API.
-3. Add one or more files, such as a model file or dataset, to your artifact object. 
+3. Add one or more files, such as a model file or dataset, to your artifact object. In this example, you'll add a single file.
 4. Log your artifact to W&B.
 
 
-```python showLineNumbers
+```python
 run = wandb.init(project="artifacts-example", job_type="add-dataset")
 artifact = wandb.Artifact(name="my_data", type="dataset")
-artifact.add_dir(local_path="./dataset.h5")  # Add dataset directory to artifact
+artifact.add_file(local_path="./dataset.h5")  # Add dataset file to artifact
 run.log_artifact(artifact)  # Logs the artifact version "my_data:v0"
 ```
 
 :::tip
-The preceding code snippet, and the [colab linked on this page](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-artifacts/Artifacts_Quickstart_with_W&B.ipynb), show how to track files by uploading them to W&B. See the [track external files](./track-external-files.md) page for information on how to add references to files or directories that are stored in external object storage (for example, in an Amazon S3 bucket). 
+See the [track external files](./track-external-files.md) page for information on how to add references to files or directories stored in external object storage, like an Amazon S3 bucket. 
 :::
 
-## How to get started
+## Download an artifact
+Indicate the artifact you want to mark as input to your run with the [`use_artifact`](../../ref/python/run.md#use_artifact) method, which returns an artifact object:
 
-Depending on your use case, explore the following resources to get started with W&B Artifacts:
+```python
+artifact = run.use_artifact("my_data:latest") #returns a run object using the "my_data" artifact
+```
+
+Then, use the returned object to download all contents of the artifact:
+
+```python
+datadir = artifact.download() #downloads the full "my_data" artifact to the default directory.
+```
+
+:::tip
+You can pass a custom path into the `root` [parameter](../../ref/python/artifact.md) to download an artifact to a specific directory. For alternate ways to download artifacts and to see additional parameters, see the guide on [downloading and using artifacts](./download-and-use-an-artifact.md)
+:::
 
-* If this is your first time using W&B Artifacts, we recommend you go through the [Artifacts Colab notebook](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/wandb-artifacts/Artifacts_Quickstart_with_W&B.ipynb#scrollTo=fti9TCdjOfHT).
-* Read the [artifacts walkthrough](./artifacts-walkthrough.md) for a step-by-step outline of the W&B Python SDK commands you could use to create, track, and use a dataset artifact.
-* Explore this chapter to learn how to:
-  * [Construct an artifact](./construct-an-artifact.md) or a [new artifact version](./create-a-new-artifact-version.md)
-  * [Update an artifact](./update-an-artifact.md)
-  * [Download and use an artifact](./download-and-use-an-artifact.md).
-  * [Delete artifacts](./delete-artifacts.md).
-* Explore the [Python SDK Artifact APIs](../../ref/python/artifact.md) and [Artifact CLI Reference Guide](../../ref/cli/wandb-artifact/README.md).
+## Next steps
+* Learn how to [version](./create-a-new-artifact-version.md), [update](./update-an-artifact.md), or [delete](./delete-artifacts.md) artifacts.
+* Learn how to trigger downstream workflows in response to changes to your artifacts with [artifact automation](./project-scoped-automations.md).
+* Learn about the [model registry](../model_registry/intro.md), a space that houses trained models.
+* Explore the [Python SDK](../../ref/python/artifact.md) and [CLI](../../ref/cli/wandb-artifact/README.md) reference guides.