Minor edits

Author: PhaniShekhar

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

@@ -243,7 +243,7 @@ image_object_detection_job = automl.image_object_detection(
 Provide a [compute target](concept-azure-machine-learning-architecture.md#compute-targets) for automated ML to conduct model training. Automated ML models for computer vision tasks require GPU SKUs and support NC and ND families. We recommend the NCsv3-series (with v100 GPUs) for faster training. A compute target with a multi-GPU VM SKU leverages multiple GPUs to also speed up training. Additionally, when you set up a compute target with multiple nodes you can conduct faster model training through parallelism when tuning hyperparameters for your model.
 
 > [!NOTE]
-> If you are using a [compute instance](concept-compute-instance.md) as your compute target, please make sure that multiple AutoML jobs are not run at once. Also, please make sure that `max_concurrent_trials` is set to 1 in your [experiment budget](#experiment-budget).
+> If you are using a [compute instance](concept-compute-instance.md) as your compute target, please make sure that multiple AutoML jobs are not run at the same time. Also, please make sure that `max_concurrent_trials` is set to 1 in your [experiment budget](#experiment-budget).
 
 The compute target is passed in using the `compute` parameter. For example:
 

articles/machine-learning/v1/how-to-auto-train-image-models-v1.md

@@ -188,7 +188,7 @@ automl_image_config = AutoMLImageConfig(training_data=training_dataset)
 Provide a [compute target](../v1/concept-azure-machine-learning-architecture.md#compute-targets) for automated ML to conduct model training. Automated ML models for computer vision tasks require GPU SKUs and support NC and ND families. We recommend the NCsv3-series (with v100 GPUs) for faster training. A compute target with a multi-GPU VM SKU leverages multiple GPUs to also speed up training. Additionally, when you set up a compute target with multiple nodes you can conduct faster model training through parallelism when tuning hyperparameters for your model.
 
 > [!NOTE]
-> If you are using a [compute instance](../concept-compute-instance.md) as your compute target, please make sure that multiple AutoML jobs are not run at once. Also, please make sure that `max_concurrent_iterations` is set to 1 in your [experiment resources](#resources-for-the-sweep).
+> If you are using a [compute instance](../concept-compute-instance.md) as your compute target, please make sure that multiple AutoML jobs are not run at the same time. Also, please make sure that `max_concurrent_iterations` is set to 1 in your [experiment resources](#resources-for-the-sweep).
 
 The compute target is a required parameter and is passed in using the `compute_target` parameter of the `AutoMLImageConfig`. For example: