Updating doc for max_concurrent_trials - incorporating pr feedback

Author: rjaincc

articles/machine-learning/how-to-auto-train-image-models.md

@@ -334,8 +334,8 @@ You can control the resources spent on your AutoML Image training job by specify
 
 Parameter | Detail
 -----|----
-`max_trials` |  Parameter for maximum number of configurations to sweep. Must be an integer between 1 and 1000. When exploring just the default hyperparameters for a given model algorithm, set this parameter to 1. Default value is 1.
-`max_concurrent_trials`| Maximum number of runs out of `max_trials` that can run concurrently. If specified, must be an integer between 1 and 100.  Default value is 1. <br><br> **NOTE:** <li> The number of concurrent runs is gated on the resources available in the specified compute target. Ensure that the compute target has the available resources for the desired concurrency.  <li> If user assigns `max_concurrent_trials` a value greater than that of `max_trials`, then  `max_concurrent_trials` would be set to a value same as that of `max_trials`. For example, user sets `max_concurrent_trials=4`, `max_trials=2`; values would be updated `max_concurrent_trials=2`, `max_trials=2`.
+`max_trials` |  Parameter for maximum number of configurations to sweep. Must be an integer between 1 and 1000. When exploring just the default hyperparameters for a given model algorithm, set this parameter to 1. The default value is 1.
+`max_concurrent_trials`| Maximum number of runs that can run concurrently. If specified, must be an integer between 1 and 100.  The default value is 1. <br><br> **NOTE:** <li> The number of concurrent runs is gated on the resources available in the specified compute target. Ensure that the compute target has the available resources for the desired concurrency.  <li> `max_concurrent_trials` is capped at `max_trials` internally. For example, if user sets `max_concurrent_trials=4`, `max_trials=2`; values would be internallly updated as `max_concurrent_trials=2`, `max_trials=2`.
 `timeout_minutes`| The amount of time in minutes before the experiment terminates. If none specified, default experiment timeout_minutes is seven days (maximum 60 days)
 
 # [Azure CLI](#tab/cli)

articles/machine-learning/how-to-auto-train-nlp-models.md

@@ -505,8 +505,8 @@ You can control the resources spent on your hyperparameter sweep by specifying t
 
 Parameter | Detail
 -----|----
-`max_trials` |  Required parameter for maximum number of configurations to sweep. Must be an integer between 1 and 1000. When exploring just the default hyperparameters for a given model algorithm, set this parameter to 1.
-`max_concurrent_trials`| Maximum number of runs out of `max_trials` that can run concurrently. If specified, must be an integer between 1 and 100.  Default value is 1. <br><br> **NOTE:** <li> The number of concurrent runs is gated on the resources available in the specified compute target. Ensure that the compute target has the available resources for the desired concurrency.  <li> If user assigns `max_concurrent_trials` a value greater than that of `max_trials`, then  `max_concurrent_trials` would be set to a value same as that of `max_trials`. For example, user sets `max_concurrent_trials=4`, `max_trials=2`; values would be updated `max_concurrent_trials=2`, `max_trials=2`.
+`max_trials` |  Parameter for maximum number of configurations to sweep. Must be an integer between 1 and 1000. When exploring just the default hyperparameters for a given model algorithm, set this parameter to 1. The default value is 1.
+`max_concurrent_trials`| Maximum number of runs that can run concurrently. If specified, must be an integer between 1 and 100.  The default value is 1. <br><br> **NOTE:** <li> The number of concurrent runs is gated on the resources available in the specified compute target. Ensure that the compute target has the available resources for the desired concurrency.  <li> `max_concurrent_trials` is capped at `max_trials` internally. For example, if user sets `max_concurrent_trials=4`, `max_trials=2`; values would be internallly updated as `max_concurrent_trials=2`, `max_trials=2`.
 
 You can configure all the sweep related parameters as shown in the example below.