Question: distinction between Modules and Models in Designer

Question

In Azure ML, under designer, there are 3 categories:

• Datasets
• Modules
• Models

Datasets are pretty straightforward, but I don't understand the distinction between Modules and Models. As an ML researcher, when I think of a "model", I think of something like linear regression or SVM. However, those are listed under Modules -> Machine Learning Algorithms. So what exactly qualifies as a Model?

Answer 1

Hi,

Thanks for reaching out.

Model is a concept of Machine Learning itself. A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data. Once you have trained the model, you can use it to reason over data that it hasn't seen before, and make predictions about those data. For example, let's say you want to build an application that can recognize a user's emotions based on their facial expressions. You can train a model by providing it with images of faces that are each tagged with a certain emotion, and then you can use that model in an application that can recognize any user's emotion.

Modules is one of the concept of Azure Machine Learning Designer. Each module represents a set of code that can run independently and perform a machine learning task, given the required inputs. A module might contain a particular algorithm, or perform a task that is important in machine learning, such as missing value replacement, or statistical analysis.

Let me know if you have any question.

Regards,
Yutong