Redis Launches Redis Feature Form: Enterprise-Grade Feature Store for Production ML
Redis has officially introduced Redis Feature Form, a comprehensive managed feature store platform designed for production machine learning environments. Following its acquisition of Featureform last October, Redis integrates this orchestration layer to manage the full feature lifecycle, including definition, pipeline orchestration, versioning, lineage, and sub-millisecond online serving. The new platform aims to reduce operational overhead and prevent data drift between training and inference phases. Key enhancements include unified batch and streaming pipelines, workspace support for multi-tenancy, fine-grained job control, atomic DAG updates, and enhanced security features like RBAC and mTLS. By expanding beyond its traditional role as merely an online serving layer, Redis now positions itself higher in the ML stack as a control plane for defining and governing features. This launch targets enterprise ML teams working on critical applications such as fraud detection, risk scoring, and personalization, offering a governed, self-service path that minimizes the need for custom glue code and homegrown pipeline maintenance.
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Redis Launches Redis Feature Form: Enterprise-Grade Feature Store for Production ML
Redis has officially introduced Redis Feature Form, a comprehensive managed feature store platform designed for production machine learning environments. Following its acquisition of Featureform last October, Redis integrates this orchestration layer to manage the full feature lifecycle, including definition, pipeline orchestration, versioning, lineage, and sub-millisecond online serving. The new platform aims to reduce operational overhead and prevent data drift between training and inference phases. Key enhancements include unified batch and streaming pipelines, workspace support for multi-tenancy, fine-grained job control, atomic DAG updates, and enhanced security features like RBAC and mTLS. By expanding beyond its traditional role as merely an online serving layer, Redis now positions itself higher in the ML stack as a control plane for defining and governing features. This launch targets enterprise ML teams working on critical applications such as fraud detection, risk scoring, and personalization, offering a governed, self-service path that minimizes the need for custom glue code and homegrown pipeline maintenance.
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