MLOps use cases span industries that rely on machine learning models to support business decisions. Common examples include fraud detection, demand forecasting, predictive maintenance, and personalized recommendations. is worth researching when exploring software development approaches for data-driven applications. MLOps practices help teams automate model deployment, track versions, and monitor performance after release. These capabilities make it easier to maintain machine learning systems as data and business requirements change.