Description
Seldon Core 2 is a production-ready, Kubernetes-native framework designed for the scalable deployment and management of machine learning (ML) and Large Language Model (LLM) systems. Its data-centric and modular architecture facilitates the seamless handling of everything from simple models to complex ML applications across diverse environments, including on-premise, hybrid, and multi-cloud setups. This approach ensures flexibility, standardization, enhanced observability, and cost efficiency in MLOps and LLMOps workflows.
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The framework's key differentiator is its flexibility, allowing real-time ML model deployment tailored to specific needs. It is platform-agnostic, supporting on-premise, cloud, and hybrid deployments with an adaptive architecture that future-proofs MLOps by enabling dynamic scaling and component reuse. This modular design enhances resource efficiency, optimizes allocation, and ensures long-term scalability and adaptability.
Standardization is another core aspect, with Seldon Core 2 enforcing best practices for ML deployment to ensure consistency, reliability, and efficiency throughout the lifecycle. By automating deployment steps, it removes operational bottlenecks, speeds up rollouts, and allows teams to concentrate on high-value tasks. The "learn once, deploy anywhere" philosophy streamlines deployment across different environments, reducing risk and boosting productivity. It supports conventional, foundational, and LLM models, fostering collaboration among MLOps Engineers, Data Scientists, and Software Engineers.
Enhanced observability is provided through real-time monitoring, analysis, and performance tracking of data pipelines, models, and deployment environments. Seldon Core 2 combines operational and data science monitoring, offering essential metrics for maintenance and decision-making. Its customizable framework simplifies operational monitoring for complex, mission-critical use cases, ensuring all prediction data is auditable for explainability, compliance, and trust.
Optimization for resource efficiency is achieved through its modular architecture, enabling the deployment of only necessary components for agility and high performance. It supports dynamic infrastructure scaling, scaling to zero for on-demand workloads, and preserving deployment state for seamless reactivation. Consolidated serving infrastructure with multi-model serving (MMS) and overcommit maximizes resource utilization and reduces compute overhead. The framework's extendability and modular adaptation allow integration with LLMs and other modules for scalable AI, maximizing value extraction and cost efficiency. Predictable, fixed pricing further supports cost-effective scaling and innovation.
- CategoryMLOps & Model Deployment
- Pricing ModelPredictable, Fixed pricing
- DeploymentKubernetes-native
- PlatformsWeb
Seldon Core 2's Core Features
Kubernetes-native framework for ML and LLM systems
Supports on-premise, hybrid, and multi-cloud deployments
Modular architecture for flexible and scalable applications
Enforces standardization and best practices for ML deployment
Provides real-time monitoring and performance tracking
Data-centric approach for auditable prediction data
Optimized resource utilization with multi-model serving
Scales infrastructure dynamically based on demand
Supports conventional, foundational, and LLM models
Enables component reuse and optimized resource allocation
Future-proofs MLOps/LLMOps with adaptive architecture
Facilitates collaboration between MLOps, Data Scientists, and Software Engineers
Getting Started with Seldon Core 2
Install Seldon Core 2: Deploy the framework on your Kubernetes cluster.
Configure your ML models: Define deployment configurations for your models.
Build and package: Prepare your models and dependencies for deployment.
Deploy models: Utilize Seldon Core 2 to deploy your ML or LLM systems.
Monitor performance: Track real-time metrics and system health.
Optimize deployments: Adjust resources and configurations for efficiency.
Seldon Core 2's Use Cases
- ML Model Deployment
- LLM System Serving
- Real-time Inference
- Hybrid Cloud MLOps
- Multi-Cloud ML Operations
- Scalable AI Applications
- Observability for ML
