Branch	Status
master
release-0.1

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

• Goals
• Prerequisites
• Quick Start
• Install
• Deployment guide
• Reference

Machine learning deployment has many challenges. Seldon Core intends to help with these challenges. Its high level goals are:

• 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

A Kubernetes Cluster.
Kubernetes can be deployed into many environments, both in cloud and on-premise.

• Quick Start using Minikube
• Jupyter Notebook showing deployment of prebuilt model using Minikube
• Jupyter Notebook showing deployment of prebuilt model using GCP cluster

Official releases can be installed via helm from the repository https://storage.googleapis.com/seldon-charts.

To install seldon-core:

helm install seldon-core --name seldon-core --repo https://storage.googleapis.com/seldon-charts

To install the optional analytics components including Prometheus and Grafana with a built-in dashboard for monitoring the running ML deployments run:

helm install seldon-core --name seldon-core \
    --set grafana_prom_admin_password=password \
    --set persistence.enabled=false \
    --repo https://storage.googleapis.com/seldon-charts

Three steps:

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

• Prediction API
• Seldon Deployment Custom Resource

• Epsilon-greedy multi-armed bandits for real time optimization of models
• Advanced graphs showing the various types of runtime prediction graphs that can be built.

• ksonnet registry at : https://github.com/SeldonIO/seldon-core/tree/master/seldon-core
• Jupyter notebook to create seldon-core with ksonnet and expose APIs using Ambassador.

• Slack Channel

• CHANGELOG
• Developer Guide