Seldon Core is an MLOps framework for deploying, managing, and scaling machine learning models and AI applications on Kubernetes.
The tool addresses the challenge of running production machine learning systems at scale by providing a standardized deployment platform that works across different model types, on-premises or in any cloud environment. It packages models with their serving infrastructure and handles the operational complexity of managing many models in production. The framework supports both individual models and modular, data-centric applications composed of multiple components.
Teams should adopt this tool if they need to serve multiple machine learning models in Kubernetes with production-grade operational capabilities. It suits organizations deploying diverse model types that want to avoid building custom serving infrastructure. The framework is particularly valuable for teams running many models simultaneously, as it offers multi-model serving on shared inference servers and an overcommit feature that allows deploying more models than available memory by managing unused model instances. The tool supports building complex AI applications through pipelines that compose multiple components with Kafka-based real-time data streaming between them, alongside capabilities for A/B testing, shadow deployments, and custom component integration.
The project maintains active development with regular updates to its core serving infrastructure and documentation. The codebase shows ongoing refinement of deployment patterns and operational features. The maintainers continue expanding the framework's capabilities for handling increasingly complex AI system architectures. Documentation is actively maintained with detailed guides covering installation, server configuration, model deployment, pipeline composition, experimentation workflows, and performance tuning.
