Does Seldon Core support explainability?

Yes, Seldon Core provides explainability algorithms (SHAP, LIME) to understand model predictions and improve transparency. The Alibi Explain Module provides extensive capabilities.

What is the LLM Module?

The LLM Module simplifies the deployment, support, and lifecycle management of GenAI applications and LLMs.

Seldon Core

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Overview

Seldon Core is an open-source MLOps platform designed to streamline the deployment, monitoring, and management of machine learning models and AI applications. Built on Kubernetes, it provides a standardized framework for deploying models from various frameworks (TensorFlow, PyTorch, scikit-learn) in production. Seldon offers features like multi-model serving, A/B testing, drift detection, and explainability to ensure models are reliable and compliant at scale. Core 2 standardizes complex AI deployments with Kubernetes-native pipelines, making GenAI and ML applications production-ready out-of-the-box. It supports real-time and batch predictions, integrates with monitoring tools, and allows for custom model serving logic. Seldon aims to avoid vendor lock-in by enabling deployment across any cloud or on-premise infrastructure. Modular architecture supports efficient resource utilization and cost reduction.

Common tasks

Model Deployment Model Monitoring Explainability Drift Detection A/B Testing

FAQ

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What is Seldon Core?

Seldon Core is an open-source MLOps platform for deploying, monitoring, and managing machine learning models on Kubernetes.

What model serving frameworks are supported?

Seldon Core supports various frameworks, including TensorFlow, PyTorch, scikit-learn, and custom model servers.

How does Seldon Core handle model versioning?

Seldon Core allows deploying multiple versions of a model and routing traffic between them for A/B testing and canary deployments.

How can I monitor model performance with Seldon Core?

Seldon Core integrates with monitoring tools like Prometheus and Grafana to track model performance metrics, such as latency, throughput, and error rate.

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