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Using Azure ML Studio
Training a Model

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Introduction to Azure Machine Learning
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Machine learning is a notoriously complex subject, which usually requires a great deal of advanced math and software development skills. That’s why it’s so amazing that Azure Machine Learning Studio lets you train and deploy machine learning models without any coding, using a drag-and-drop interface. With this web-based software, you can create applications for predicting everything from customer churn rates, to image classifications, to compelling product recommendations.

In this course, you will learn the basic concepts of machine learning, and then follow hands-on examples of choosing an algorithm, running data through a model, and deploying a trained model as a predictive web service.

Learning Objectives

  • Prepare data for use by an Azure Machine Learning Studio experiment
  • Train a machine learning model in Azure Machine Learning Studio
  • Deploy a trained model to make predictions

Intended Audience

  • Anyone who is interested in machine learning


  • No mandatory prerequisites
  • Azure account recommended (sign up for free trial at https://azure.microsoft.com/free if you don’t have an account)

This Course Includes

  • 54 minutes of high-definition video
  • Many hands-on demos



I hope you enjoyed learning about Azure Machine Learning Studio. Let’s do a quick review of what you learned.

Supervised learning is where you feed data that contains the correct answer (or label) for every row of data. The machine learning algorithm then tries to find a model that uses the other features of the data to determine the correct answer.

In unsupervised learning, the data doesn’t have labels, so the algorithm tries to find patterns in the data instead.

ML Studio supported three types of supervised learning algorithms. You use regression when you need to predict a number, such as a house price or a temperature. You use classification when you need to predict which category an instance falls into, such as a cat picture or a low credit risk. And you use anomaly detection when you’re looking for unusual data, such as network intrusion attempts or credit fraud.

ML Studio only supports one type of unsupervised learning algorithm. A clustering algorithm divides data into groups that have similar characteristics.

When choosing an algorithm, use Microsoft’s machine learning algorithm cheat sheet. If you need to choose a regression or classification algorithm, then Decision Forest is usually a good choice, because it has fast training times and high accuracy.

Overfitting occurs when a model matches training data so closely that it doesn’t generalize well to other data. At a minimum, you should divide your data into training and test datasets, and then check your model’s accuracy against the test dataset. Yet another advantage of the Decision Forest algorithm is that it usually avoids overfitting by design.

We covered some of the basic pre-processing steps that are often necessary to prepare your data for use with a machine learning model. First you should remove all rows with a missing label value. It’s also usually a good idea to remove columns that have lots of missing values. For the remaining missing values, you should experiment with different methods of handling them, such as removing rows where they occur or replacing them with a value. Finally, you can often reduce the training time and improve the accuracy of a model by removing some of the features.

When you deploy a trained model as a web service, it provides two types of prediction services. The request/response service lets you submit a single row of data and it returns a prediction for that data instance. The batch service lets you submit a file with many data points and it returns a copy of the file with a column of predictions.

If you want to add your own custom code to an experiment, bear in mind that ML Studio doesn’t provide any development or debugging support, so you’ll need to do that in another environment first. ML Studio supports scripts written in R and Python. A custom module contains a script plus the specifications for the module itself, including input and output ports, parameters, etc. The only language supported by custom modules is R.

Now you know how to prepare data for use by an Azure Machine Learning Studio experiment; train a machine learning model in Azure ML Studio; and deploy a trained model as a web service to make predictions.

To learn more about Azure ML Studio, you can read Microsoft’s documentation. Also watch for new machine learning courses on Cloud Academy, because we’re always publishing new courses.

If you have any questions or comments, please let me know in the Comments tab below this video or by emailing support@cloudacademy.com. Thanks and keep on learning!

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About the Author
Learning paths63

Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).