As organizations increasingly integrate machine learning (ML) into products and operations, the need for scalable, automated pipelines—known as MLOps (Machine Learning Operations)—has become critical. Traditionally, MLOps frameworks are built for centralized training pipelines where data, models, and compute resources reside in the cloud or a private data center.
However, the rise of Federated Learning (FL) is disrupting this paradigm. Federated Learning allows training across decentralized edge devices while keeping data local, often due to privacy regulations or data sovereignty requirements. Managing ML lifecycles under this model introduces new challenges—and demands a new operational strategy known as Federated MLOps.
This article compares Federated MLOps and Traditional MLOps, exploring their differences across architecture, automation, privacy, monitoring, deployment, and scalability.
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