Machine Learning Deployment Framework for Kubernetes
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Dockerize the entier wrapping process of building sklearn_iris example
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Seldon Core

Seldon Core is an open source framework for deploying machine learning models on Kubernetes.


Seldon Core goals:

  • Allow data scientists to create models using any machine learning toolkit or programming language. We plan to initially cover the tools/languages below:
    • Python based models including
      • Tensorflow models
      • Sklearn models
    • Spark Models
    • H2O Models
  • Expose machine learning models via REST and gRPC automatically when deployed for easy integration into business apps that need predictions.
  • Allow complex runtime inference graphs to be deployed as microservices. These graphs can be composed of:
    • Models - runtime inference executable for machine learning models
    • Routers - route API requests to sub-graphs. Examples: AB Tests, Multi-Armed Bandits.
    • Combiners - combine the responses from sub-graphs. Examples: ensembles of models
    • Transformers - transform request or responses. Example: transform feature requests.
  • Handle full lifecycle management of the deployed model:
    • Updating the runtime graph with no downtime
    • Scaling
    • Monitoring
    • Security

Quick Start


Official releases can be installed via helm from the repository For example:

helm install seldon-core --name seldon-core \
    --set grafana_prom_admin_password=password \
    --set persistence.enabled=false \

Deployment Guide


Three steps:

  1. Wrap your runtime prediction model.
  2. Define your runtime inference graph in a seldon deployment custom resource.
  3. Deploy the graph.