Build and Publish an Azure Machine Learning Project

Azure Machine Learning (AML) is used for designing and deploying machine learning models at scale. Here, we will explore the development of an AML project in Azure. We’ll go through the step-by-step process of creating an AML workspace, setting up a computing environment, building a machine-learning model, and finally publishing the project.

Azure Machine Learning

Azure Machine Learning (AML) is an integrated service within the Azure ecosystem, offering a comprehensive environment for building, testing, and deploying machine learning models. It supports various programming languages and allows the design of diverse ML solutions, including supervised, unsupervised, and deep learning. AML automates the entire ML life cycle, integrating with other Azure services like Databricks, Data Factory, and Synapse Analytics. Users, including those new to ML, can leverage pre-built algorithms and models for seamless integration with Azure’s ecosystem.

The image below illustrates a sample Azure Machine Learning workflow:

Components of the AML resource

Azure Machine Learning has several components that work together to enable end-to-end machine learning workflows. Some of the major components are as follows:

1. Azure machine learning studio: A web-based tool for building, deploying, and managing machine learning models. It provides a drag-and-drop interface for creating experiments and pipelines.

2. Azure ML SDK: A Python-based software development kit (SDK) for building and deploying machine learning models. It provides a flexible and programmable way to create machine-learning solutions.

3. Azure ML designer: A drag-and-drop visual interface for building machine learning models without requiring any programming skills. It allows users to create, test, and deploy machine learning models using pre-built modules.

4. Azure machine learning compute: A managed compute infrastructure that allows users to create and manage clusters of virtual machines ...