Merge pull request #106856 from nibaccam/dsets-maintenance

Data & SKlearn | Freshness update

Author: PRMerger15

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

@@ -8,7 +8,7 @@ ms.subservice: core
 ms.topic: conceptual
 ms.author: maxluk
 author: maxluk
-ms.date: 08/02/2019
+ms.date: 03/09/2020
 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.
@@ -37,7 +37,7 @@ Run this code on either of these environments:
     - [Create a workspace configuration file](how-to-configure-environment.md#workspace).
     - Download the dataset and sample script file 
         - [iris dataset](https://archive.ics.uci.edu/ml/datasets/iris)
-        - [`train_iris.py`](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn)
+        - [train_iris.py](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn)
     - You can also find a completed [Jupyter Notebook version](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/ml-frameworks/scikit-learn/training/train-hyperparameter-tune-deploy-with-sklearn/train-hyperparameter-tune-deploy-with-sklearn.ipynb) of this guide on the GitHub samples page. The notebook includes an expanded section covering intelligent hyperparameter tuning and retrieving the best model by primary metrics.
 
 ## Set up the experiment
@@ -155,7 +155,7 @@ run = experiment.submit(estimator)
 run.wait_for_completion(show_output=True)
 ```
 
-As the Run is executed, it goes through the following stages:
+As the run is executed, it goes through the following stages:
 
 - **Preparing**: A docker image is created according to the TensorFlow estimator. The image is uploaded to the workspace's container registry and cached for later runs. Logs are also streamed to the run history and can be viewed to monitor progress.
 
@@ -177,7 +177,7 @@ import joblib
 joblib.dump(svm_model_linear, 'model.joblib')
 ```
 
-Register the model to your workspace with the following code. By specifying the parameters `model_framework`, `model_framework_version`, and `resource_configuration`, no-code model deployment becomes available. This allows you to directly deploy your model as a web service from the registered model, and the `ResourceConfiguration` object defines the compute resource for the web service.
+Register the model to your workspace with the following code. By specifying the parameters `model_framework`, `model_framework_version`, and `resource_configuration`, no-code model deployment becomes available. This allows you to directly deploy your model as a web service from the registered model, and the [`ResourceConfiguration`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.resource_configuration.resourceconfiguration?view=azure-ml-py) object defines the compute resource for the web service.
 
 ```Python
 from azureml.core import Model
@@ -197,7 +197,7 @@ contains a section on registering models, but you can skip directly to [creating
 
 ### (Preview) No-code model deployment
 
-Instead of the traditional deployment route, you can also use the no-code deployment feature (preview)for scikit-learn. No-code model deployment is supported for all built-in scikit-learn model types. By registering your model as shown above with the `model_framework`, `model_framework_version`, and `resource_configuration` parameters, you can simply use the `deploy()` static function to deploy your model.
+Instead of the traditional deployment route, you can also use the no-code deployment feature (preview)for scikit-learn. No-code model deployment is supported for all built-in scikit-learn model types. By registering your model as shown above with the `model_framework`, `model_framework_version`, and `resource_configuration` parameters, you can simply use the [`deploy()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model%28class%29?view=azure-ml-py#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false-) static function to deploy your model.
 
 ```python
 web_service = Model.deploy(ws, "scikit-learn-service", [model])

articles/machine-learning/how-to-train-with-datasets.md

@@ -10,16 +10,16 @@ ms.author: sihhu
 author: MayMSFT
 manager: cgronlun
 ms.reviewer: nibaccam
-ms.date: 09/25/2019
+ms.date: 03/09/2020
 
-# Customer intent: As an experienced Python developer, I need to make my data available to my remote compute to train my machine learning models.
+# Customer intent: As an experienced Python developer, I need to make my data available to my local or remote compute to train my machine learning models.
 
 ---
 
 # Train with datasets in Azure Machine Learning
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
-In this article, you learn the two ways to consume [Azure Machine Learning datasets](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset%28class%29?view=azure-ml-py) in remote experiment training runs without worrying about connection strings or data paths.
+In this article, you learn the two ways to consume [Azure Machine Learning datasets](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset%28class%29?view=azure-ml-py) in a remote experiment training runs without worrying about connection strings or data paths.
 
 - Option 1: If you have structured data, create a TabularDataset and use it directly in your training script.
 
@@ -31,7 +31,7 @@ Azure Machine Learning datasets provide a seamless integration with Azure Machin
 
 To create and train with datasets, you need:
 
-* An Azure subscription. 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.
+* An Azure subscription. 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.
 
 * An [Azure Machine Learning workspace](how-to-manage-workspace.md).
 

articles/machine-learning/how-to-version-track-datasets.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: sihhu
 author: sihhu
 ms.reviewer: nibaccam
-ms.date: 11/04/2019
+ms.date: 03/09/2020
 ms.custom: 
 
 # Customer intent: As a data scientist, I want to version and track datasets so I can use and share them across multiple machine learning experiments.
@@ -57,6 +57,7 @@ titanic_ds = titanic_ds.register(workspace = workspace,
                                  description = 'titanic training data',
                                  create_new_version = True)
 ```
+You can also register a new version of a dataset at 
 
 ### Retrieve a dataset by name
 
@@ -117,7 +118,7 @@ dataset2.register(workspace = workspace,
 
 You can use a dataset as the input and output of each Machine Learning pipeline step. When you rerun pipelines, the output of each pipeline step is registered as a new dataset version.
 
-Because Machine Learning pipelines populate the output of each step into a new folder every time the pipeline reruns, the versioned output datasets are reproducible.
+Because Machine Learning pipelines populate the output of each step into a new folder every time the pipeline reruns, the versioned output datasets are reproducible. Learn more about [datasets in pipelines](how-to-create-your-first-pipeline.md#steps).
 
 ```Python
 from azureml.core import Dataset
@@ -166,7 +167,7 @@ input_dataset = inputs[0]['dataset']
 input_dataset.to_path()
 ```
 
-You can also find the `input_datasets` from experiments by using [Azure Machine Learning studio](https://ml.azure.com/). 
+You can also find the `input_datasets` from experiments by using https://ml.azure.com/. 
 
 The following image shows where to find the input dataset of an experiment on Azure Machine Learning studio. For this example, go to your **Experiments** pane and open the **Properties** tab for a specific run of your experiment, `keras-mnist`.
 
@@ -180,7 +181,9 @@ model = run.register_model(model_name='keras-mlp-mnist',
                            datasets =[('training data',train_dataset)])
 ```
 
-After registration, you can see the list of models registered with the dataset by using Python or [Azure Machine Learning studio](https://ml.azure.com/). The following view is from the **Datasets** pane under **Assets**. Select the dataset and then select the **Models** tab for a list of the models that are registered with the dataset. 
+After registration, you can see the list of models registered with the dataset by using Python or go to https://ml.azure.com/.
+
+The following view is from the **Datasets** pane under **Assets**. Select the dataset and then select the **Models** tab for a list of the models that are registered with the dataset. 
 
 ![Input datasets models](./media/how-to-version-track-datasets/dataset-models.png)