Seldon Core : Open Infrastructure for Scalable AI Model Serving

Seldon is an open-source platform focused on deploying, scaling, and monitoring machine learning models in production. Built with enterprise needs in mind, Seldon provides a Kubernetes-native infrastructure for serving AI models using industry-standard protocols. It is designed for MLOps teams, data scientists, and infrastructure engineers who require flexible, reliable, and observable model serving at scale.

Seldon supports any ML framework, including TensorFlow, PyTorch, ONNX, XGBoost, and scikit-learn. It also integrates with popular CI/CD tools, model explainability libraries, and monitoring systems. With capabilities for canary deployments, advanced traffic routing, and multi-model serving, Seldon makes it easier to manage the operational complexity of machine learning systems.

What are the main features of Seldon?

Framework-agnostic model serving

Seldon lets teams deploy models from any machine learning library using a standard interface.

• Support for REST and gRPC protocols
• Compatible with TensorFlow, PyTorch, MLflow, Hugging Face, and more
• Wraps models into reusable containers (Seldon Deployments or Inference Graphs)

This enables standardized model deployment across languages and frameworks.

Kubernetes-native architecture

Seldon is built to run natively on Kubernetes, offering seamless integration with cloud-native infrastructure.

• Each model runs as a containerized microservice
• Horizontal autoscaling using Kubernetes-native policies
• Infrastructure-as-code deployment with Helm or Kustomize

This allows easy scaling and orchestration of complex inference workloads.

Advanced orchestration and routing

Seldon supports dynamic routing and composition of models for more complex applications.

• Create inference graphs that combine multiple models or processing steps
• Implement A/B tests, shadow deployments, and canary rollouts
• Configure routing logic based on headers, payloads, or metadata

These capabilities are ideal for testing, experimentation, and gradual release strategies.

Built-in monitoring and observability

Seldon provides observability features for performance, traffic, and model behavior.

• Integrates with Prometheus, Grafana, and OpenTelemetry
• Tracks metrics like request rate, latency, error rate, and custom model outputs
• Supports drift detection and model explainability through integrations with Alibi and other tools

This helps maintain model reliability and detect issues in production environments.

Model explainability and auditability

Seldon includes features to understand, explain, and audit model predictions.

• Integrates with Alibi for feature attribution, counterfactuals, and uncertainty estimates
• Supports logging and versioning of prediction requests and responses
• Compatible with enterprise-grade governance and compliance practices

Useful for regulated industries or high-risk AI applications where transparency is essential.

Why choose Seldon?

• Framework-independent deployment: Serve any model, from any library, in any language.
• Built for Kubernetes: Native compatibility with cloud-native workflows and infrastructure.
• Advanced model orchestration: Combine and route models flexibly in production systems.
• Integrated observability: Monitor traffic, performance, drift, and explainability in real time.
• Enterprise-ready: Designed for scale, auditability, and regulatory compliance.