Seldon Core is an open-source platform used in MLOps to deploy, scale, and manage machine learning models on Kubernetes in production environments.
It provides a standardized way to serve ML models as microservices, enabling organizations to operationalize machine learning workflows efficiently.
Key capabilities of Seldon Core:
1. Model deployment on Kubernetes
Seldon Core allows ML models to be deployed as containerized microservices on Kubernetes clusters.
2. Multi-framework support
It supports popular ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and custom models.
3. Scalable model serving
Enables horizontal scaling of models based on traffic and workload demands.
4. A/B testing and canary deployments
Supports safe model rollout strategies to compare model performance before full deployment.
5. Monitoring and observability
Integrates with monitoring tools to track model performance, latency, and prediction behavior.
6. Advanced deployment patterns
Supports inference graphs, model pipelines, and ensemble models for complex ML workflows.
How Seldon Core is used in MLOps:
- Deploying trained models into production environments
- Managing model lifecycle (versioning, updates, rollback)
- Enabling CI/CD pipelines for ML workflows
- Performing controlled rollout strategies like canary releases
- Monitoring model drift and performance in production
Benefits of Seldon Core:
- Simplifies ML model deployment on Kubernetes
- Improves scalability and reliability of ML services
- Enables production-grade MLOps practices
- Supports flexible deployment and experimentation strategies
- Integrates easily with DevOps and observability tools
Conclusion:
Seldon Core in MLOps is a powerful Kubernetes-based framework that enables organizations to deploy, manage, and scale machine learning models in production efficiently while supporting advanced deployment strategies and observability.