Merge pull request #87030 from sdgilley/patch-9

Update tutorial-train-models-with-aml.md

Author: PRMerger16

articles/machine-learning/service/tutorial-train-models-with-aml.md

@@ -16,7 +16,7 @@ ms.custom: seodec18
 
 # Tutorial: Train image classification models with MNIST data and scikit-learn using Azure Machine Learning
 
-In this tutorial, you train a machine learning model on remote compute resources. You'll use the training and deployment workflow for Azure Machine Learning service (preview) in a Python Jupyter notebook.  You can then use the notebook as a template to train your own machine learning model with your own data. This tutorial is **part one of a two-part tutorial series**.  
+In this tutorial, you train a machine learning model on remote compute resources. You'll use the training and deployment workflow for Azure Machine Learning service in a Python Jupyter notebook.  You can then use the notebook as a template to train your own machine learning model with your own data. This tutorial is **part one of a two-part tutorial series**.  
 
 This tutorial trains a simple logistic regression by using the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset and [scikit-learn](https://scikit-learn.org) with Azure Machine Learning service. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28 x 28 pixels, representing a number from zero to nine. The goal is to create a multiclass classifier to identify the digit a given image represents.
 
@@ -33,7 +33,7 @@ You learn how to select a model and deploy it in [part two of this tutorial](tut
 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.
 
 >[!NOTE]
-> Code in this article was tested with Azure Machine Learning SDK version 1.0.41.
+> Code in this article was tested with [Azure Machine Learning SDK](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py) version 1.0.57.
 
 ## Prerequisites