Update concept-automated-ml.md

Author: j-martens

articles/machine-learning/concept-automated-ml.md

@@ -12,9 +12,9 @@ ms.author: sacartac
 ms.date: 04/22/2020
 ---
 
-# What is automated machine learning?
+# What is automated machine learning (AutoML)?
 
-Automated machine learning, also referred to as automated ML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Automated ML is based on a breakthrough from our [Microsoft Research division](https://arxiv.org/abs/1705.05355).
+Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality. Automated ML is based on a breakthrough from our [Microsoft Research division](https://arxiv.org/abs/1705.05355).
 
 Traditional machine learning model development is resource-intensive, requiring significant domain knowledge and time to produce and compare dozens of models. With automated machine learning, you'll accelerate the time it takes to get production-ready ML models with great ease and efficiency.
 
@@ -60,7 +60,7 @@ Advanced forecasting configuration includes:
 
 See examples of regression and automated machine learning for predictions in these Python notebooks: [Sales Forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb), [Demand Forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb), and [Beverage Production Forecast](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb).
 
-## How automated ML works
+## How AutoML works
 
 During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to "fit" your data.  It will stop once it hits the exit criteria defined in the experiment. 
 
@@ -151,9 +151,9 @@ See how to convert to ONNX format [in this Jupyter notebook example](https://git
 
 The ONNX runtime also supports C#, so you can use the model built automatically in your C# apps without any need for recoding or any of the network latencies that REST endpoints introduce. Learn more about [inferencing ONNX models with the ONNX runtime C# API](https://github.com/Microsoft/onnxruntime/blob/master/docs/CSharp_API.md). 
 
-## Automated ML in studio vs SDK
+## The studio vs SDK
 
-Learn about the parity and differences between the high-level automated ML capabilities available through the Python SDK and the studio. 
+Learn about the parity and differences between the high-level automated ML capabilities available through the Python SDK and the studio in Azure Machine Learning. 
 
 <a name="parity"></a>