Seldon-core

Overview

Seldon-core is an open-source MLOps framework for packaging, deploying, monitoring, and managing thousands of machine learning models in production. It runs on Kubernetes and provides custom resources for inference graphs, A/B testing, and model monitoring.

Best for

Best for
Teams deploying and monitoring many machine learning models in production on Kubernetes

Use cases

• Deploying machine learning models to production on Kubernetes
• Monitoring model performance and detecting drift
• Managing model lifecycle with canary deployments and rollbacks

Notes

Seldon-core is an open-source MLOps framework for packaging, deploying, monitoring, and managing thousands of machine learning models in production. It runs on Kubernetes and provides custom resources for inference graphs, A/B testing, and model monitoring.

4,752 stars on GitHub. Last updated 2026-03-23.

Use cases

• Deploying machine learning models to production on Kubernetes
• Monitoring model performance and detecting drift
• Managing model lifecycle with canary deployments and rollbacks

Pros

• Open source with a large community and 4752 GitHub stars
• Supports multiple ML frameworks and languages
• Scalable to thousands of models with built-in monitoring

Cons

• Requires Kubernetes expertise to set up and operate
• Complex configuration for advanced deployment patterns
• Documentation can be sparse for some edge cases

Indexed from awesome-llmops and enriched against its public facts.