Description
Seldon Core 2 is a comprehensive MLOps and LLMOps framework designed for the deployment, management, and scaling of AI systems within Kubernetes environments. It provides a standardized approach to deploying diverse model types, whether on-premises or in any cloud, ensuring production-ready applications out of the box. This framework is particularly adept at handling singular models as well as complex, modular, and data-centric applications.
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The framework's capabilities extend to building composable AI applications through pipelines, which can leverage technologies like Kafka for real-time data streaming between different components. It offers robust autoscaling features for both models and application components, driven by native or custom logic. For cost efficiency, Seldon Core supports multi-model serving, allowing consolidation of multiple models onto shared inference servers, and overcommit functionality to deploy more models than available memory permits, reducing infrastructure costs for underutilized resources.
Seldon Core also facilitates experimentation through its routing capabilities, enabling A/B tests and shadow deployments for candidate models or pipelines. Users can implement custom logic, drift and outlier detection, and integrate with Large Language Models (LLMs) through plug-and-play custom components, seamlessly integrating with Seldon's broader ecosystem of ML/AI products. The framework is influenced by research into the next generation of ML model serving frameworks, aiming to address key desiderata for advanced model serving.
Installation and configuration are managed within Kubernetes, with extensive documentation available for servers, models, pipelines, experiments, and performance tuning. Seldon Core is distributed under the terms of the Business Source License, with all contributions also licensed under this term. The project is actively developed, with a strong community presence and regular updates, making it a powerful tool for organizations looking to operationalize their AI initiatives at scale.
- CategoryMLOps & Model Deployment
- Pricing ModelOpen source
- DeploymentKubernetes
Seldon Core's Core Features
MLOps and LLMOps framework
Kubernetes-native deployment
Standardized model deployment
Modular and data-centric AI applications
Composable AI pipelines
Real-time data streaming with Kafka
Autoscaling for models and components
Multi-model serving for cost efficiency
Overcommit for increased model density
A/B testing and shadow deployments
Custom component integration (LLMs, drift detection)
Production-ready out-of-the-box
On-premise and cloud deployment support
Getting Started with Seldon Core
Deploy: Package and deploy your machine learning models and AI applications on Kubernetes.
Configure: Set up pipelines, autoscaling, and multi-model serving configurations.
Integrate: Incorporate custom components for advanced logic and LLMs.
Monitor: Utilize built-in monitoring capabilities for production systems.
Manage: Oversee and manage thousands of production machine learning models.
Optimize: Tune performance and experiment with different model routing strategies.
Seldon Core's Use Cases
- Model Deployment
- AI Application Orchestration
- Real-time Inference
- Cost Optimization
- Model Experimentation
- LLMOps
- Data-Centric AI
