End to End ML pipeline using Azure Machine Learning Studio Designer

Azure Machine Learning is a cloud service for accelerating and managing the machine learning project lifecycle. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows: Train and deploy models, and manage MLOps.

You can create a model in Azure Machine Learning or use a model built from an open-source platform, such as Pytorch, TensorFlow, or scikit-learn. MLOps tools help you monitor, retrain, and redeploy models.

In this article, we will try to create a ML pipeline using Azure machine Learning studio. We have multiple ways to create a pipeline, which includes

• Automated ML
• Designer
• Notebook

Automated ML helps us to create pipeline without having to know much about the various data science algorithms available. We have an option to run multiple set of algorithm for a dataset and pick the best one.

Using Azure Designer we can create ML pipeline by dragging and dropping the required components and is super easy to use. The prerequisite for this is to create a workspace in Azure Machine learning studio as described below.

We must have an Azure subscription to https://portal.azure.com. However we have option to use their free credit if applicable (https://azure.microsoft.com/en-in/free/)

In the Azure dashboard, search for Azure Machine Learning and create a workspace as shown below. We can use the default settings itself while creating the workspace.

Once the workspace is created, Launch the same. ML studio will be open in a new tab (you may be asked to login again).