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Getting Started

  • Quickstart Guide
  • Overview of Components
  • Install on Kubernetes
  • Join the Community

Seldon Core Deep Dive

  • Detailed Installation Parameters
  • Pre-packaged Inference Servers
  • Language Wrappers for Custom Models
  • Create your Inference Graph
  • Deploy your Model
  • Testing your Model Endpoints
  • Python Module and Client
  • Troubleshooting guide
  • Usage reporting
  • Upgrading
  • Changelog

Pre-Packaged Inference Servers

  • MLflow Server
  • SKLearn Server
  • Triton Inference Server
  • Tensorflow Serving
  • XGBoost Server

Production

  • Supported API Protocols
  • CI/CD MLOps at Scale
  • Metrics with Prometheus
  • Model Metadata
  • Payload Logging with ELK
  • Distributed Tracing with Jaeger
  • Replica Scaling
  • Budgeting Disruptions
  • Custom Inference Servers

Language Wrappers

  • Python Language Wrapper

Incubating Projects

  • Java Language Wrapper
  • Java (JNI) Language Wrapper [ALPHA]
  • C++ Language Wrapper [ALPHA]
  • R Language Wrapper [ALPHA]
  • NodeJS Language Wrapper [ALPHA]
  • Go Language Wrapper [ALPHA]

Ingress

  • Ambassador Ingress
  • Istio Ingress
  • OpenShift

Streaming and Batch Processing

  • Overview of Batch Processing
  • Stream Processing with KNative
  • Native Kafka Integration

Advanced Inference

  • Model Explanations
  • Outlier Detection
  • Drift Detection
  • Routers (incl. Multi Armed Bandits)

Examples

  • Notebooks
  • Articles/Blogs
    • Stream-Based Model Serving with Cloudflow and Seldon
    • Seldon Inference Graph: Serving your Models in a Pipeline
    • Open Source Model Management Roundup
    • Polyaxon, Argo and Seldon for model training
    • Manage ML Deployments Like A Boss
    • Using PyTorch 1.0 and ONNX with Fabric for Deep Learning
    • AI on Kubernetes - O'Reilly Tutorial
    • Scalable Data Science - The State of DevOps/MLOps in 2018
    • Overview of Openshift source-to-image use in Seldon-Core
    • CartPole game by Reinforcement Learning
    • Clear Linux Deep Learning Reference Stack
    • MLOps: The end of end-to-end
    • What would machine learning look like if you mixed in DevOps?
    • [Archive] Move Fast and Break Things? The AI Governance Dilemma
    • 开源史海钩沉系列 Seldon Core
  • Videos
  • Podcasts
  • Kubeflow Pipelines

Reference

  • Annotation-based Configuration
  • AWS Marketplace Install
  • Benchmarking
  • General Availability
  • Helm Charts
  • Images
  • Logging & Log Level
  • Private Docker Registry
  • Prediction APIs
  • Python API reference
  • Release Highlights
  • Seldon Deployment CRD
  • Service Orchestrator
  • Kubeflow
  • Concepts

Developer

  • Overview
  • Contributing to Seldon Core
  • End to End Tests
  • Roadmap
  • Build using private repo
seldon-core
  • Docs »
  • Blogs
  • Edit on GitHub

Blogs¶

Blog articles featuring different use cases of Seldon:

  • Stream-Based Model Serving with Cloudflow and Seldon
  • Seldon Inference Graph: Serving your Models in a Pipeline
  • Open Source Model Management Roundup
  • Polyaxon, Argo and Seldon for model training
  • Manage ML Deployments Like A Boss
  • Using PyTorch 1.0 and ONNX with Fabric for Deep Learning
  • AI on Kubernetes - O'Reilly Tutorial
  • Scalable Data Science - The State of DevOps/MLOps in 2018
  • Overview of Openshift source-to-image use in Seldon-Core
  • CartPole game by Reinforcement Learning
  • Clear Linux Deep Learning Reference Stack
  • MLOps: The end of end-to-end
  • What would machine learning look like if you mixed in DevOps?
  • [Archive] Move Fast and Break Things? The AI Governance Dilemma
  • 开源史海钩沉系列 Seldon Core
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