MORE branding changes

Author: j-martens

articles/machine-learning/service/how-to-track-experiments.md

@@ -1,6 +1,6 @@
 ---
 title: Log ML experiments & metrics
-titleSuffix: Azure Machine Learning service
+titleSuffix: Azure Machine Learning
 description: Monitor your Azure ML experiments and monitor run metrics to enhance the model creation process. Add logging to your training script and view the logged results of a run.  Use run.log, Run.start_logging, or ScriptRunConfig.
 services: machine-learning
 author: heatherbshapiro
@@ -17,10 +17,10 @@ ms.custom: seodec18
 
 # Monitor Azure ML experiment runs and metrics
 
-Enhance the model creation process by tracking your experiments and monitoring run metrics. In this article, learn how to add logging code to your training script, submit an experiment run, monitor that run, and inspect the results in Azure Machine Learning service.
+Enhance the model creation process by tracking your experiments and monitoring run metrics. In this article, learn how to add logging code to your training script, submit an experiment run, monitor that run, and inspect the results in Azure Machine Learning.
 
 > [!NOTE]
-> Azure Machine Learning service may also log information from other sources during training, such as automated machine learning runs, or the Docker container that runs the training job. These logs are not documented. If you encounter problems and contact Microsoft support, they may be able to use these logs during troubleshooting.
+> Azure Machine Learning may also log information from other sources during training, such as automated machine learning runs, or the Docker container that runs the training job. These logs are not documented. If you encounter problems and contact Microsoft support, they may be able to use these logs during troubleshooting.
 
 ## Available metrics to track
 
@@ -61,7 +61,7 @@ Before adding logging and submitting an experiment, you must set up the workspac
 
 **start_logging** creates an interactive run for use in scenarios such as notebooks. Any metrics that are logged during the session are added to the run record in the experiment.
 
-The following example trains a simple sklearn Ridge model locally in a local Jupyter notebook. To learn more about submitting experiments to different environments, see [Set up compute targets for model training with Azure Machine Learning service](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets).
+The following example trains a simple sklearn Ridge model locally in a local Jupyter notebook. To learn more about submitting experiments to different environments, see [Set up compute targets for model training with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets).
 
 1. Create a training script in a local Jupyter notebook. 
 
@@ -87,7 +87,7 @@ The following example trains a simple sklearn Ridge model locally in a local Jup
    joblib.dump(value = reg, filename = 'model.pkl');
    ```
 
-2. Add experiment tracking using the Azure Machine Learning service SDK, and upload a persisted model into the experiment run record. The following code adds tags, logs, and uploads a model file to the experiment run.
+2. Add experiment tracking using the Azure Machine Learning SDK, and upload a persisted model into the experiment run record. The following code adds tags, logs, and uploads a model file to the experiment run.
 
    ```python
     # Get an experiment object from Azure Machine Learning

articles/machine-learning/service/how-to-train-chainer.md

@@ -1,6 +1,6 @@
 ---
 title: Train deep learning neural network with Chainer 
-titleSuffix: Azure Machine Learning service
+titleSuffix: Azure Machine Learning
 description: Learn how to run your PyTorch training scripts at enterprise scale using Azure Machine Learning's Chainer estimator class.  The example script classifis handwritten digit images to build a deep learning neural network using the Chainer Python library running on top of numpy. 
 services: machine-learning
 ms.service: machine-learning
@@ -13,7 +13,7 @@ ms.date: 08/02/2019
 #Customer intent: As a Python Chainer developer, I need to combine open-source with a cloud platform to train, evaluate, and deploy my deep learning models at scale. 
 ---
 
-# Train and register Chainer models at scale with Azure Machine Learning service
+# Train and register Chainer models at scale with Azure Machine Learning
 
 In this article, learn how to run your [Chainer](https://chainer.org/) training scripts at enterprise scale using Azure Machine Learning's [Chainer estimator](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.chainer?view=azure-ml-py) class. 
 The example training script in this article uses the popular [MNIST dataset](http://yann.lecun.com/exdb/mnist/) to classify handwritten digits using a deep neural network (DNN) built using the Chainer Python library running on top of [numpy](https://www.numpy.org/).
@@ -22,7 +22,7 @@ Whether you're training a deep learning Chainer model from the ground-up or you'
 
 Learn more about [deep learning vs machine learning](concept-deep-learning-vs-machine-learning.md).
 
-If you don’t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning service](https://aka.ms/AMLFree) today.
+If you don’t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today.
 
 ## Prerequisites
 
@@ -57,7 +57,7 @@ print("SDK version:", azureml.core.VERSION)
 
 ### Initialize a workspace
 
-The [Azure Machine Learning service workspace](concept-workspace.md) is the top-level resource for the service. It provides you with a centralized place to work with all the artifacts you create. In the Python SDK, you can access the workspace artifacts by creating a [`workspace`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) object.
+The [Azure Machine Learning workspace](concept-workspace.md) is the top-level resource for the service. It provides you with a centralized place to work with all the artifacts you create. In the Python SDK, you can access the workspace artifacts by creating a [`workspace`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) object.
 
 Create a workspace object by reading the `config.json` file created in the [prerequisites section](#prerequisites):
 
@@ -206,7 +206,7 @@ for f in run.get_file_names():
 
 ## Next steps
 
-In this article, you trained and registered a deep learning, neural network using Chainer on Azure Machine Learning service. To learn how to deploy a model, continue on to our  [model deployment](how-to-deploy-and-where.md) article.
+In this article, you trained and registered a deep learning, neural network using Chainer on Azure Machine Learning. To learn how to deploy a model, continue on to our  [model deployment](how-to-deploy-and-where.md) article.
 
 * [Tune hyperparameters](how-to-tune-hyperparameters.md)
 

articles/machine-learning/service/how-to-train-keras.md

@@ -1,7 +1,7 @@
 ---
 title: Train deep learning neural network with Keras
-titleSuffix: Azure Machine Learning service
-description: Learn how to train and register a Keras deep neural network classification model running on TensorFlow using Azure Machine Learning service.
+titleSuffix: Azure Machine Learning
+description: Learn how to train and register a Keras deep neural network classification model running on TensorFlow using Azure Machine Learning.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -15,13 +15,13 @@ ms.custom: seodec18
 #Customer intent: As a Python Keras developer, I need to combine open-source with a cloud platform to train, evaluate, and deploy my deep learning models at scale. 
 ---
 
-# Train and register a Keras classification model with Azure Machine Learning service
+# Train and register a Keras classification model with Azure Machine Learning
 
-This article shows you how to train and register a Keras classification model built on TensorFlow using Azure Machine Learning service. It uses the popular [MNIST dataset](http://yann.lecun.com/exdb/mnist/) to classify handwritten digits using a deep neural network (DNN) built using the [Keras Python library](https://keras.io) running on top of [TensorFlow](https://www.tensorflow.org/overview).
+This article shows you how to train and register a Keras classification model built on TensorFlow using Azure Machine Learning. It uses the popular [MNIST dataset](http://yann.lecun.com/exdb/mnist/) to classify handwritten digits using a deep neural network (DNN) built using the [Keras Python library](https://keras.io) running on top of [TensorFlow](https://www.tensorflow.org/overview).
 
-Keras is a high-level neural network API capable of running top of other popular DNN frameworks to simplify development. With Azure Machine Learning service, you can rapidly scale out training jobs using elastic cloud compute resources. You can also track your training runs, version models, deploy models, and much more.
+Keras is a high-level neural network API capable of running top of other popular DNN frameworks to simplify development. With Azure Machine Learning, you can rapidly scale out training jobs using elastic cloud compute resources. You can also track your training runs, version models, deploy models, and much more.
 
-Whether you're developing a Keras model from the ground-up or you're bringing an existing model into the cloud, Azure Machine Learning service can help you build production-ready models.
+Whether you're developing a Keras model from the ground-up or you're bringing an existing model into the cloud, Azure Machine Learning can help you build production-ready models.
 
 See the [conceptual article](concept-deep-learning-vs-machine-learning.md) for information on the differences between machine learning and deep learning.
 
@@ -65,7 +65,7 @@ from azureml.core.compute_target import ComputeTargetException
 
 ### Initialize a workspace
 
-The [Azure Machine Learning service workspace](concept-workspace.md) is the top-level resource for the service. It provides you with a centralized place to work with all the artifacts you create. In the Python SDK, you can access the workspace artifacts by creating a [`workspace`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) object.
+The [Azure Machine Learning workspace](concept-workspace.md) is the top-level resource for the service. It provides you with a centralized place to work with all the artifacts you create. In the Python SDK, you can access the workspace artifacts by creating a [`workspace`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) object.
 
 Create a workspace object from the `config.json` file created in the [prerequisites section](#prerequisites).
 
@@ -200,7 +200,7 @@ for f in run.get_file_names():
 
 ## Next steps
 
-In this article, you trained and registered a Keras model on Azure Machine Learning service. To learn how to deploy a model, continue on to our model deployment article.
+In this article, you trained and registered a Keras model on Azure Machine Learning. To learn how to deploy a model, continue on to our model deployment article.
 
 > [!div class="nextstepaction"]
 > [How and where to deploy models](how-to-deploy-and-where.md)

articles/machine-learning/service/how-to-train-ml-models.md

@@ -1,7 +1,7 @@
 ---
 title: Train ML models with estimators
-titleSuffix: Azure Machine Learning service
-description: Learn how to perform single-node and distributed training of traditional machine learning and deep learning models using Azure Machine Learning services Estimator class
+titleSuffix: Azure Machine Learning
+description: Learn how to perform single-node and distributed training of traditional machine learning and deep learning models using Azure Machine Learning Estimator class
 ms.author: maxluk
 author: maxluk
 services: machine-learning

articles/machine-learning/service/how-to-train-pytorch.md

@@ -1,6 +1,6 @@
 ---
 title: Train deep learning neural network with PyTorch
-titleSuffix: Azure Machine Learning service
+titleSuffix: Azure Machine Learning
 description: Learn how to run your PyTorch training scripts at enterprise scale using Azure Machine Learning's PyTorch estimator class.  The example scripts classify chicken and turkey images to build a deep learning neural network based on PyTorch's transfer learning tutorial. 
 services: machine-learning
 ms.service: machine-learning
@@ -64,7 +64,7 @@ from azureml.train.dnn import PyTorch
 
 ### Initialize a workspace
 
-The [Azure Machine Learning service workspace](concept-workspace.md) is the top-level resource for the service. It provides you with a centralized place to work with all the artifacts you create. In the Python SDK, you can access the workspace artifacts by creating a [`workspace`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) object.
+The [Azure Machine Learning workspace](concept-workspace.md) is the top-level resource for the service. It provides you with a centralized place to work with all the artifacts you create. In the Python SDK, you can access the workspace artifacts by creating a [`workspace`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) object.
 
 Create a workspace object from the `config.json` file created in the [prerequisites section](#prerequisites).
 
@@ -90,15 +90,15 @@ The dataset consists of about 120 training images each for turkeys and chickens,
 
 ### Prepare training scripts
 
-In this tutorial, the training script, `pytorch_train.py`, is already provided. In practice, you can take any custom training script, as is, and run it with Azure Machine Learning service.
+In this tutorial, the training script, `pytorch_train.py`, is already provided. In practice, you can take any custom training script, as is, and run it with Azure Machine Learning.
 
 Upload the Pytorch training script, `pytorch_train.py`.
 
 ```Python
 shutil.copy('pytorch_train.py', project_folder)
 ```
 
-However, if you would like to use Azure Machine Learning service tracking and metrics capabilities, you will have to add a small amount code inside your training script. Examples of metrics tracking can be found in `pytorch_train.py`.
+However, if you would like to use Azure Machine Learning tracking and metrics capabilities, you will have to add a small amount code inside your training script. Examples of metrics tracking can be found in `pytorch_train.py`.
 
 ## Create a compute target
 
@@ -186,7 +186,7 @@ for f in run.get_file_names():
 
 ## Distributed training
 
-The [`PyTorch`](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml-py) estimator also supports distributed training across CPU and GPU clusters. You can easily run distributed PyTorch jobs and Azure Machine Learning service will manage the orchestration for you.
+The [`PyTorch`](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.dnn.pytorch?view=azure-ml-py) estimator also supports distributed training across CPU and GPU clusters. You can easily run distributed PyTorch jobs and Azure Machine Learning will manage the orchestration for you.
 
 ### Horovod
 [Horovod](https://github.com/uber/horovod) is an open-source, all reduce framework for distributed training developed by Uber. It offers an easy path to distributed GPU PyTorch jobs.
@@ -219,7 +219,7 @@ To optimize inference with the [ONNX Runtime](concept-onnx.md), convert your tra
 
 ## Next steps
 
-In this article, you trained and registered a deep learning, neural network using PyTorch on Azure Machine Learning service. To learn how to deploy a model, continue on to our model deployment article.
+In this article, you trained and registered a deep learning, neural network using PyTorch on Azure Machine Learning. To learn how to deploy a model, continue on to our model deployment article.
 
 > [!div class="nextstepaction"]
 > [How and where to deploy models](how-to-deploy-and-where.md)

articles/machine-learning/service/how-to-train-scikit-learn.md

@@ -1,6 +1,6 @@
 ---
 title: Train machine learning models with scikit-learn
-titleSuffix: Azure Machine Learning service
+titleSuffix: Azure Machine Learning
 description: Learn how to run your scikit-learn training scripts at enterprise scale using Azure Machine Learning's SKlearn estimator class. The example scripts classify iris flower images to build a machine learning model based on scikit-learn's iris dataset.
 services: machine-learning
 ms.service: machine-learning
@@ -14,7 +14,7 @@ ms.custom: seodec18
 #Customer intent: As a Python scikit-learn developer, I need to combine open-source with a cloud platform to train, evaluate, and deploy my machine learning models at scale.
 ---
 
-# Build scikit-learn models at scale with Azure Machine Learning service
+# Build scikit-learn models at scale with Azure Machine Learning
 
 In this article, learn how to run your scikit-learn training scripts at enterprise scale using Azure Machine Learning's [SKlearn estimator](https://docs.microsoft.com/python/api/azureml-train-core/azureml.train.sklearn.sklearn?view=azure-ml-py) class. 
 
@@ -62,7 +62,7 @@ from azureml.core.compute_target import ComputeTargetException
 
 ### Initialize a workspace
 
-The [Azure Machine Learning service workspace](concept-workspace.md) is the top-level resource for the service. It provides you with a centralized place to work with all the artifacts you create. In the Python SDK, you can access the workspace artifacts by creating a [`workspace`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) object.
+The [Azure Machine Learning workspace](concept-workspace.md) is the top-level resource for the service. It provides you with a centralized place to work with all the artifacts you create. In the Python SDK, you can access the workspace artifacts by creating a [`workspace`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.workspace.workspace?view=azure-ml-py) object.
 
 Create a workspace object from the `config.json` file created in the [prerequisites section](#prerequisites).
 
@@ -190,7 +190,7 @@ model = run.register_model(model_name='sklearn-iris', model_path='model.joblib')
 ## Next steps
 
 
-In this article, you trained and registered a Keras model on Azure Machine Learning service. To learn how to deploy a model, continue on to our model deployment article.
+In this article, you trained and registered a Keras model on Azure Machine Learning. To learn how to deploy a model, continue on to our model deployment article.
 
 > [!div class="nextstepaction"]
 > [How and where to deploy models](how-to-deploy-and-where.md)