Subject archive for "mlops," page 3

Machine learning operations (MLOps) is more than just the latest buzzword in the artificial intelligence (AI) and machine learning (ML) community. Many companies are realizing they need a set of practices to efficiently get models built and into production, and MLOps does just that.

Today’s businesses are investing heavily in data science – spending on software, hardware and services is projected to break the $500 billion mark by 2024, according to IDC. Data science models with machine learning (ML) and artificial intelligence (AI) techniques have proven their worth at forging new revenue streams and upending entire industries. For a model-driven business, new revenue from such use cases can range from hundreds of millions to billions of dollars. At successful companies, leaders have built a well-oiled analytical flywheel to create a steady flow of models that can tap this new gold rush.

Model validation is a core component of developing machine learning or artificial intelligence (ML/AI). While it’s separate from training and deployment, it should pervade the entire data science lifecycle.

Editor’s note: This is part of a series of articles sharing best practices from companies developing an enterprise data science strategy. Some articles will include information about their use of Domino.