[doc] Add documentation for Aim artifacts logging (#3115)

* [doc] Add documentation for Aim artifacts logging

* [doc] Add paragraph for boto3 credentials handling

* [fix] Typos in documentation

docs/source/index.rst

@@ -44,6 +44,7 @@
    using/remote_tracking.md
    using/training_monitoring.md
    using/logging.md
+   using/artifacts.md
    using/notifications.md
    using/callbacks.md
    using/k8s_deployment.md

docs/source/using/artifacts.md

@@ -0,0 +1,47 @@
+##  Logging artifacts with Aim
+
+One of the key aspects of experiment tracking is the ability to link certain
+files/artifacts to the training run metadata. Examples of such artifacts are the
+model checkpoints, training run configuration files, etc.
+
+Starting from the version 3.19, Aim provides the logging API for artifacts, and the
+UI for showing artifact's metadata.
+
+### Artifacts logging SDK
+
+There are only two steps for logging artifacts with Aim:
+1. Set the artifacts storage URI for the Aim Run:
+```python
+import aim
+
+run = aim.Run()
+# Use S3 as artifacts storage
+run.set_artifacts_uri('s3://aim/artifacts/')
+```
+Aim will create directory with the name of `run.hash` and store all artifacts there. 
+Note that setting artifacts storage URI is required only once per run.
+2.  Log artifact object:
+```python
+# Log run configuration files as artifacts
+run.log_artifact('config.yaml', name='run-config')
+```
+
+Once logged, artifact metadata will appear in the Aim UI Run details page:
+![](../_static/images/ui/run_details/run-overview-artifacts.png)
+
+
+### Storage backends
+
+Training artifacts may represent large files, and, depending on the use case, might require
+different storage backends. For example, in case of small runs using the local file system,
+or network shared FS might be a good option. In case of the large models, cloud-based object
+stores, such as AWS S3, may be a better choice.
+
+When the artifacts URI is set, Aim will detect storage backend based on the URI scheme.
+Currently, S3 is the only supported backend for artifacts storage.
+
+#### S3 Artifacts Storage Backend
+
+Aim uses `boto3` Python package for accessing AWS resources. No additional credentials
+validation os done on the Aim side. More details on how credentials configuration is done
+for `boto3` is available [here](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html).