MLOps Use Cases: 12 Practical Examples Teams Run in Production

MLOps focuses on repeatable production machine learning workflows, not just experimental notebooks, delivering measurable results. This article details 12 use cases with breakdowns of production challenges and MLOps solutions. It provides templates, including data needs, deployment types, risks, KPIs, and pitfalls, helpful for various roles in technology. The guide includes guidance on choosing initial use cases and the components of a lean MLOps stack, with AppRecode as a potential implementation partner. The article emphasizes MLOps' role in combining engineering, DevOps, and data practices for reliable model deployment and maintenance. The covered use cases span finance, retail, manufacturing, logistics, and SaaS industries. Each case is tied to specific risks, data needs, and key performance indicators. Investing in the underlying infrastructure early allows faster scaling. The initial case discussed is real-time fraud detection. Other examples include demand forecasting, predictive maintenance, personalization, and customer support.