Automating ML Workflows with Azure MLOps

As machine learning becomes more widely adopted, it is essential to have efficient and consistent workflows for developing, deploying, and monitoring ML models. Azure MLOps is an integrated toolchain for building, testing, and deploying ML models at scale. Here, we will discuss the various aspects of automating ML workflows with Azure MLOps using Azure Data Factory.

MLOps (Machine Learning Operations)

MLOps (Machine Learning Operations) is an extension of DevOps principles and practices that aims to standardize and automate the end-to-end machine learning life cycle. MLOps involves using tools, processes, and automation to manage, monitor, and improve the machine learning model development process, from data preparation to model deployment and maintenance.

Azure provides a suite of tools and services that make it easy to implement MLOps, including Azure Machine Learning service, Azure DevOpsAzure DevOps is a software as a service (SaaS) platform from Microsoft that provides tools for developing and deploying software. It was formerly known as Visual Studio Team Services (VSTS)., Azure Kubernetes Service (AKS)Azure Kubernetes Service (AKS) offers the quickest way to start developing and deploying cloud-native apps in Azure, datacenters, or at the edge, with built-in code-to-cloud pipelines and guardrails. , Azure Container RegistryAzure Container Registry (ACR) is a cloud-based service for managing and storing Docker container images and related artifacts., and more. By leveraging these tools, users can create a streamlined, automated pipeline for machine-learning projects, allowing them to develop, deploy, and manage models more efficiently and effectively.

The diagram below explains a sample machine learning operations workflow in Azure:

Azure MLOps components

In the Azure ecosystem, MLOps is considered a blend of three services that are responsible for building, deploying, and maintaining machine learning projects: