Introducing Azure Machine Learning – Machine Learning Fundamental Concepts

Introducing Azure Machine Learning

Developers can build, deploy, and improve high-quality machine learning models with Azure’s machine learning tools and user-friendly platform. It adds more value to a project by reducing the time to value with industry-leading machine learning operations (MLOps), open source interoperability, and integrated tools. This trusted platform is designed for responsible AI applications in machine learning. In addition, Microsoft Azure manages the Azure Machine Learning service offering.

Today, a lot of software engineers, data scientists, and machine learning professionals use the Azure Machine Learning service to build machine learning applications. These applications do things like train and deploy machine learning models and manage machine learning pipelines.

To use Azure Machine Learning, you need a Microsoft Azure subscription. Below in the Table 3-1 you understand the various Azure Machine learning components.

Table 3-1Important components of Azure Machine Learning

Azure ML Components	Description
Azure Machine Learning workspace	This is a centralized, top-level place to work on our projects. With all the artifacts and tools to work with, this workspace stores training logs, metrics, output, and script snapshots. Machine learning tasks read and write artifacts to your workspace, such as when you run an experiment to train a model; it logs the job run results to the workspace; and when you use Automated ML to train a model, it writes training results to a workspace.
Compute	Azure Compute provides developers with all the infrastructure they need. Whether it is for building an app or deploying it, Compute has got it. It is an infrastructure-as-a-service platform for hosting and managing application workloads. It comes with a wide range of service offerings, such as virtual machines, the Azure Container Service, Azure App Services, Azure Batch, and Azure Service Fabric.
Data	A cloud-based data integration service that allows one to initiate and automate data movement and transformation. This service comes in handy when supporting data migrations, getting data from a server or online data to an Azure Data Lake, carrying out various data integration processes, and integrating data from different ERP systems and loading it into Azure Synapse for reporting.
Jobs	Microsoft Azure job defines, schedules, monitors, and controls operations related to Microsoft Azure virtual machines. You can add one or more Microsoft Azure jobs to the job stream that automates your business process flow, to provide a flexible and dynamic allocation of cloud resources to your workload.
Services	When you are ready to start using the model in your applications, you can publish those applications as services that can be accessed through public and private endpoints.