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SeldonIO/seldon-core

An MLOps and LLMOps framework for deploying, managing, and scaling modular, data-centric AI applications and models on Kubernetes.

Core Features

Deploy composable AI applications with real-time data streaming via Kafka.

Automate autoscaling for models and application components based on native or custom logic.

Consolidate multiple models on shared inference servers to optimize infrastructure costs.

Conduct A/B tests and shadow deployments with experiment routing.

Integrate custom logic, drift detection, and LLMs through plug-and-play components.

Detailed Introduction

Seldon Core 2 is a robust MLOps and LLMOps framework designed for deploying, managing, and scaling diverse AI systems within Kubernetes environments. It enables the standardized deployment of everything from singular models to complex, modular, and data-centric applications, supporting both on-premise and any cloud infrastructure. The platform emphasizes operational efficiency and cost optimization through features like multi-model serving, which consolidates models on shared inference servers, and overcommit capabilities. Furthermore, it provides advanced experimentation tools, including A/B tests and shadow deployments, making it a comprehensive, production-ready solution for managing thousands of machine learning models at scale.