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MLOps
(Machine Learning Operations)
MLOps is a set of practices that aims to deploy and maintain ML models in production reliably and efficiently. Combining aspects of DevOps with data science, MLOps addresses the unique challenges of operationalizing machine learning systems.
Key components include version control for models and data, continuous integration/delivery pipelines (CI/CD), and monitoring for model drift. The MLOps market is growing rapidly as organizations recognize that most ML projects fail during deployment. By establishing standardized workflows and automation, MLOps helps bridge the gap between experimental models and production-ready AI systems that deliver business value.
Key components include version control for models and data, continuous integration/delivery pipelines (CI/CD), and monitoring for model drift. The MLOps market is growing rapidly as organizations recognize that most ML projects fail during deployment. By establishing standardized workflows and automation, MLOps helps bridge the gap between experimental models and production-ready AI systems that deliver business value.