Update articles/machine-learning/how-to-manage-quotas.md

Co-authored-by: Mope Akande <[email protected]>

Author: SimranArora904

articles/machine-learning/how-to-manage-quotas.md

@@ -123,7 +123,9 @@ The following table shows more limits in the platform. Reach out to the Azure Ma
 <sup>2</sup> Jobs on a low-priority node can be preempted whenever there's a capacity constraint. We recommend that you implement checkpoints in your job.
 
 ### Azure Machine Learning shared quota
-Azure Machine Learning is introducing a concept of shared quota, where a pool of quota is shared across different users and regions concurrently. Upon availability, quota from the shared pool can temporarily be used for a short period of time (varies depending on the use case) for testing purposes. This eliminates the need to file a support ticket for quota increase or wait for the quota request to be approved in order to proceed with your workload. Today, shared quota is available for running Spark jobs and for testing Llama inferencing in Model Catalog for a trial period. Note that shared quota should be used only for creating temporary test endpoints, not production endpoints. For a production experience, you should request for dedicated quota by filing a support ticket. Billing for shared quota has the same billing model as dedicated virtual machine families with a usage-based billing model.
+Azure Machine Learning provides a pool of shared quotas that is available for different users across various regions to use concurrently. Depending upon availability, users can temporarily access quota from the shared pool, and use the quota to perform testing for a limited amount of time. The specific time duration depends on the use case. By temporarily using quota from the quota pool, you no longer need to file a support ticket for a short-term quota increase or wait for your quota request to be approved before you can proceed with your workload. 
+
+Use of the shared quota pool is available for running Spark jobs and for testing inferencing for Llama models from the Model Catalog. You should use the shared quota only for creating temporary test endpoints, not production endpoints. For endpoints in production, you should request dedicated quota by filing a support ticket. Billing for shared quota is usage-based, just like billing for dedicated virtual machine families.
 
 ### Azure Machine Learning managed online endpoints