Update articles/machine-learning/how-to-safely-rollout-online-endpoints.md

Co-authored-by: SeokJin Han <[email protected]>

Author: msakande

articles/machine-learning/how-to-safely-rollout-online-endpoints.md

@@ -566,7 +566,7 @@ Though `green` has 0% of traffic allocated, you can still invoke the endpoint an
 Once you've tested your `green` deployment, you can *mirror* (or copy) a percentage of the live traffic to it. Traffic mirroring (also called shadowing) doesn't change the results returned to clients—requests still flow 100% to the `blue` deployment. The mirrored percentage of the traffic is copied and submitted to the `green` deployment so that you can gather metrics and logging without impacting your clients. Mirroring is useful when you want to validate a new deployment without impacting clients. For example, you can use mirroring to check if latency is within acceptable bounds or to check that there are no HTTP errors. Testing the new deployment with traffic mirroring/shadowing is also known as [shadow testing](https://microsoft.github.io/code-with-engineering-playbook/automated-testing/shadow-testing/). The deployment receiving the mirrored traffic (in this case, the `green` deployment) can also be called the *shadow deployment*.
 
 Mirroring has the following limitations:
-* Mirroring is supported for the CLI (v2) (version 2.4.0 or above) and Python SDK (v2) (version 1.0.0 or above). If you use an older version of CLI/SDK or Studio UI to update an endpoint, you'll lose the mirror traffic setting.
+* Mirroring is supported for the CLI (v2) (version 2.4.0 or above) and Python SDK (v2) (version 1.0.0 or above). If you use an older version of CLI/SDK to update an endpoint, you'll lose the mirror traffic setting.
 * Mirroring isn't currently supported for Kubernetes online endpoints.
 * You can mirror traffic to only one deployment in an endpoint.
 * The maximum percentage of traffic you can mirror is 50%. This limit is to reduce the effect on your [endpoint bandwidth quota](how-to-manage-quotas.md#azure-machine-learning-managed-online-endpoints) (default 5 MBPS)—your endpoint bandwidth is throttled if you exceed the allocated quota. For information on monitoring bandwidth throttling, see [Monitor managed online endpoints](how-to-monitor-online-endpoints.md#metrics-at-endpoint-scope).