{"id":11193,"date":"2022-06-29T10:15:27","date_gmt":"2022-06-29T09:15:27","guid":{"rendered":"https:\/\/ee.yelkdev.site\/?p=11193"},"modified":"2024-12-10T02:25:12","modified_gmt":"2024-12-10T02:25:12","slug":"why-mlops-should-be-at-the-top-of-your-to-do-list","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/tech-focus\/why-mlops-should-be-at-the-top-of-your-to-do-list\/","title":{"rendered":"Why MLOps should be at the top of your to-do list"},"content":{"rendered":"

If you\u2019re new to the world of MLOps, here\u2019s what you need to know: MLOps (which stands for machine learning operations) is a set of tools and ideas that help data scientists and operations teams to develop, deploy and monitor models in the AI world.<\/p>\n

That\u2019s a big deal because organisations that want to deliver AI projects often struggle to get projects off the ground at scale, and to deliver effective return on investment (ROI). Using MLOps helps those organisations to create machine learning models in a manner that is effective, consistent and scalable.<\/p>\n

Over the last decade, machine learning has become a critical resource for many organisations. Using ML models, companies can create models that can analyse vast quantities of structured and unstructured data, making predictions about business outcomes that can be used to inform faster, better decisions. The challenge, increasingly, is how those organisations monitor and manage multiple ML models and iterations.<\/p>\n

MLOps brings discipline and structure to AI<\/h2>\n

That\u2019s where MLOps comes in. While DevOps focuses on how systems are developed with regard to security, compliance and IT resource management, MLOps focuses on the consistent development of scalable models. Blending machine learning with traditional devops models creates an MLOps process that streamlines and automates the way that intelligent applications are developed, deployed and updated.<\/p>\n

Examples of how MLOps is being used include:<\/p>\n