Seldon Overview
Seldon is a comprehensive MLOps platform designed to help enterprises deploy, manage, and optimize machine learning models at scale, ensuring seamless governance, risk, and compliance management.
Key Products
Seldon offers two main products: Seldon Enterprise Platform and Seldon Core .
Seldon Enterprise Platform
This platform is tailored for enterprises needing advanced features to operationalize ML models. Here are its key features:
- Model Deployment and Management: Quickly put ML models into production using deployment wizards, and manage high volumes of models efficiently. The platform supports canary and A/B testing for continuous model optimization.
- Operational Performance and Risk Reduction: Monitor ML models proactively to respond to unexpected behavior, minimize errors, and ensure operational performance. Features include alerting systems and alignment with the Open Inference Protocol for industry standardization.
- Governance and Compliance: Ensure compliance with features like GitOps for version control, audit logs to track system changes, a model catalog for centralized metadata management, and granular user management for access control.
- Flexibility and Versatility: The platform is cloud-agnostic, allowing deployment on various cloud providers (Google, AWS, etc.) or on-premise environments. It offers access through UI, API, CLI, and SDK, avoiding vendor lock-in.
- Productivity and Cost Savings: Customers have reported up to 85% productivity gains and 60% cost savings on infrastructure through intelligent resource optimization.
Seldon Core
This is an enterprise-grade solution built on the open-source Seldon Core, providing additional support and reliability.
- Platform-Agnostic: Seldon Core can run on Kubernetes, Docker, or Docker Compose, and supports various service meshes. It leverages the Open Inference Protocol for interoperability between models.
- Advanced ML Capabilities: Core includes features like advanced metrics, request logging, model explainers, outlier detectors, A/B tests, and canary deployments. It also supports multiple ML runtimes and integrates with tools like Prometheus, Grafana, and Jaeger for comprehensive monitoring and tracing.
- Enterprise Support: Core comes with an enterprise-grade warranty, dedicated customer success managers, exclusive support channels, and defined SLAs to ensure minimal downtime and high reliability.
- Scalability and Flexibility: The platform allows users to start small and scale their ML ecosystem as needed, with flexible and extensible resource definitions. It supports models, pipelines, experiments, and servers, and provides traceability and auditing for entire inference pipelines.
Key Functionality
- Model Serving: Seldon converts ML models into production-ready REST/GRPC microservices, supporting various frameworks like TensorFlow, PyTorch, and H2O. It provides pre-packaged inference servers and custom language wrappers for different programming languages.
- Monitoring and Logging: The platform includes robust monitoring and logging capabilities, integrating with tools like Elasticsearch for request logging and Jaeger for distributed tracing.
- Compliance and Governance: Features such as audit logs, GitOps, and granular user management ensure that all changes are tracked and controlled, meeting the needs of highly regulated industries.
- Performance Optimization: Seldon offers continuous deployment strategies, including A/B testing and canary deployments, to ensure peak model performance and quick response to incidents.
Conclusion
In summary, Seldon provides a robust MLOps platform that streamlines the deployment, management, and optimization of machine learning models, while ensuring compliance, governance, and high operational performance.