Update concept-automated-ml.md

Author: j-martens

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

@@ -61,7 +61,12 @@ Learn more with this how-to: [automated machine learning for time series forecas
 
 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).
 
-## Design automated ML experiments
+## How automated ML 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. 
+
+The following diagram illustrates this process. 
+![Automated Machine learning](./media/concept-automated-ml/automl-concept-diagram2.png)
 
 Using **Azure Machine Learning**, you can design and run your automated ML training experiments with these steps:
 
@@ -77,12 +82,7 @@ Using **Azure Machine Learning**, you can design and run your automated ML train
 
 1. **Submit the training run.**
 
-## How automated ML works
-
-During training, Azure Machine Learning creates a number of in parallel pipelines that try different algorithms and parameters. 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. The following diagram illustrates this process. 
-
-  ![Automated Machine learning](./media/concept-automated-ml/automl-concept-diagram2.png)
-
+1. **Review the results** 
 
 You can also inspect the logged run information, which [contains metrics](how-to-understand-automated-ml.md) gathered during the run. The training run produces a Python serialized object (`.pkl` file) that contains the model and data preprocessing.
 
@@ -138,6 +138,8 @@ The [Caruana ensemble selection algorithm](http://www.niculescu-mizil.org/papers
 
 See the [how-to](how-to-configure-auto-train.md#ensemble) for changing default ensemble settings in automated machine learning.
 
+<a name="use-with-onnx"></a>
+
 ## AutoML & ONNX
 
 With Azure Machine Learning, you can use automated ML to build a Python model and have it converted to the ONNX format. Once the models are in the ONNX format, they can be run on a variety of platforms and devices. Learn more about [accelerating ML models with ONNX](concept-onnx.md).