Azure Mlops Machine Learning Operations Overview K21academy

November 13, 2025 by Deepak Kumar Sharma 2 Comments <img decoding="async" width="16" height="16" alt="Loading" src="https://k21academy.com/wp-content/plugins/page-views-count/ajax-loader-2x.gif" =0 title="Azure MLOps : Machine Learning Operations Overview"> Azure MLOps or Machine Learning Operations is based on DevOps principles and practices that increase the efficiency of workflows and improve the quality and consistency of the machine learning solutions. In this blog, we are going to learn more about MLOps, architecture describing how to implement continuous integration (CI), continuous delivery (CD), and retraining pipeline for an AI application using Azure Machine Learning and... In this blog, we will cover the following topics: Machine learning operations (MLOps) applies DevOps principles to machine learning projects.

Learn about which DevOps principles help in scaling a machine learning project from experimentation to production. Some familiarity with machine learning and Azure Machine Learning. Would you like to request an achievement code? Get familiar with DevOps principles and tools relevant for MLOps workloads. Learn how to work with source control for your machine learning projects. Source control is an essential part of machine learning operations (MLOps).

MLOps or Machine Learning Operations is based on DevOps principles and practices that increase the efficiency of workflows and improves the quality and consistency of the machine learning solutions. MLOps is covered in our DP-100 Design & Implement a Data Science solution on Azure training. Want to know more about MLOps? Read the blog post at https://k21academy.com/dp10021 to learn more. The blog post covers: • Overview of MLOps • Architecture of MLOps for Python Models Using Azure ML Service • MLOps Pipelines • Getting Started With MLOpsPython (Demo) Join our FREE CLASS on Microsoft Certified Azure Data Scientist Associate at k21academy.com/dp10002 and take your career to next level 🤩

Also, don’t forget to join us on our FREE Telegram group https://t.me/k21microsoftazure, and be the first to receive Microsoft Azure related news and updates. MLOps or Machine Learning Operations is based on DevOps principles and practices that increase the efficiency of workflows and improves the quality and consistency of the machine learning solutions. MLOps is a Machine Learning engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops). It applies the DevOps principles and practices like continuous integration, delivery, and deployment to the machine learning process, with an aim for faster experimentation, development, and deployment of Azure machine learning models into production... Here is a list of MLOps capabilities provided by Azure Machine Learning https://k21academy.com/ Learn Cloud From Experts Learn In-Demand tech skills with latest courses & step by step hands on labs in Oracle, Azure, AWS & DevOps.

Learning On the go View more posts This course bridges the gap between Data Science, DevOps, and Cloud by teaching you how to deploy, monitor, and manage ML models efficiently using industry-best MLOps practices. Δdocument.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); function accordion_expand_me(what, id) { var hasClass = jQuery(what + '-' + id + ' .list_arrow').hasClass('expand'); console.log(hasClass); if(!hasClass) { cb_flip_collapse_all('.learndash_navigation_lesson_topics_list'); } return cb_flip_expand_collapse(what, id); } function cb_flip_collapse_all(what) { jQuery( what + ' .list_arrow.flippable' ).removeClass( 'expand'... 1. Design and implement a machine learning model for a specific task (e.g., predictive analysis, classification, or regression).

2. Experiment with feature engineering techniques to optimize model performance. Machine learning operations (MLOps) applies DevOps principles to machine learning projects. In this learning path, you'll learn how to implement key concepts like source control, automation, and CI/CD to build an end-to-end MLOps solution. Would you like to request an achievement code? Learn how to take your machine learning model from experimentation to production by using Azure Machine Learning jobs.

Learn how to automate your machine learning workflows by using GitHub Actions. Learn how to protect your main branch and how to trigger tasks in the machine learning workflow based on changes to the code. In today's rapidly evolving technological landscape, the demand for artificial intelligence (AI) and machine learning (ML) solutions has surged. Businesses across various sectors are leveraging these technologies to improve decision-making, enhance customer experiences, and streamline operations. However, developing and deploying machine learning models is not without its challenges. This is where Azure MLOps comes into play, providing a structured approach to managing the ML lifecycle.

Before diving into Azure MLOps, it’s essential to understand what MLOps is. MLOps, or Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate the lifecycle of machine learning models. This includes: The goal of MLOps is to improve collaboration between data scientists and IT operations, ensure reproducibility, and streamline the end-to-end ML workflow. Azure MLOps refers to the implementation of MLOps practices on Microsoft Azure, a cloud computing platform that offers a variety of tools and services for data science, machine learning, and AI. Azure provides a robust environment for developing, deploying, and managing ML models at scale, making it easier for organizations to adopt and integrate machine learning into their operations.

Access to this page requires authorization. You can try signing in or changing directories. Access to this page requires authorization. You can try changing directories. This article describes three Azure architectures for machine learning operations that have end-to-end continuous integration and continuous delivery (CI/CD) pipelines and retraining pipelines. The architectures are for these AI applications:

These architectures are the product of the MLOps v2 project. They incorporate best practices that solution architects identified in the process of developing various machine learning solutions. The result is deployable, repeatable, and maintainable patterns. All three architectures use the Azure Machine Learning service. For an implementation with sample deployment templates for MLOps v2, see Azure MLOps v2 GitHub repository.

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