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  • About
    • Production-ready ML Serving Framework
    • Seldon Core Features
    • Concepts
    • Architecture
  • Installation
    • Installation Overview
    • Learning Environment
      • Self-hosted Kafka
    • Production Environment
      • Kafka Integration
      • Managed Kafka
      • Ingress Controller
    • Test the Installation
    • Advanced Configurations
      • Helm Configuration
      • Server Config
      • Server Runtime
      • Seldon Config
      • Pipeline Config
    • Upgrading
  • User Guide
    • Kubernetes Resources
    • Servers
      • Resource allocation
        • Example: Serving models on dedicated GPU nodes
    • Models
      • Multi-Model Serving
      • Inference Artifacts
      • Scheduling
      • rClone
      • Parameterized Models
      • Pandas Query
      • Storage Secrets
    • Inference
      • Inference Server
      • Run Inference
      • OIP
      • Batch
    • Pipelines
    • Scaling
      • Model Autoscaling
      • Server Autoscaling
      • HPA Autoscaling in single-model serving
    • Data Science Monitoring
      • Dataflow with Kafka
      • Model Performance Metrics
      • Drift Detection
      • Outlier Detection
      • Explainability
    • Operational Monitoring
      • Operational Metrics
      • Observability
      • Usage Metrics
      • Local Metrics
      • Tracing
    • Experiments
    • Examples
      • Local examples
      • Kubernetes examples
      • Huggingface models
      • Model zoo
      • Artifact versions
      • Pipeline examples
      • Pipeline to pipeline examples
      • Explainer examples
      • Custom Servers
      • Local experiments
      • Experiment version examples
      • Inference examples
      • Tritonclient examples
      • Batch Inference examples (kubernetes)
      • Batch Inference examples (local)
      • Checking Pipeline readiness
      • Multi-Namespace Kubernetes
      • Huggingface speech to sentiment with explanations pipeline
      • Production image classifier with drift and outlier monitoring
      • Production income classifier with drift, outlier and explanations
      • Conditional pipeline with pandas query model
      • Kubernetes Server with PVC
  • Integrations
    • Service Meshes
      • Ambassador
      • Istio
      • Traefik
      • Secure Model Endpoints
  • Resources
    • Security
    • APIs
      • Internal
        • Chainer
        • Agent
      • Inference
        • Open Inference Protocol
      • Scheduler
      • Seldon CLI
        • CLI
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          • Config Activate
          • Config Deactivate
          • Config Add
          • Config List
          • Config Remove
        • Experiment
          • Experiment Start
          • Experiment Status
          • Experiment List
          • Experiment Stop
        • Model
          • Model Status
          • Model Load
          • Model List
          • Model Infer
          • Model Metadata
          • Model Unload
        • Pipeline
          • Pipeline Load
          • Pipeline Status
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          • Pipeline Inspect
          • Pipeline Infer
          • Pipeline Unload
        • Server
          • Server List
          • Server Status
    • FAQs
    • Development
      • License
      • Release
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  1. Integrations

Service Meshes

PreviousKubernetes Server with PVCNextAmbassador

Last updated 4 months ago

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The Seldon models and pipelines are exposed via a single service endpoint in the install namespace called seldon-mesh. All models, pipelines and experiments can be reached via this single Service endpoint by setting appropriate headers on the inference REST/gRPC request. By this means Seldon is agnostic to any service mesh you may wish to use in your organisation. We provide some example integrations for some example service meshes below (alphabetical order):

We welcome help to extend these to other service meshes.

Ambassador
Istio
Traefik