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Machine Learning Deployment for Kubernetes
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cliveseldon Merge pull request #823 from jklaise/serving-test-gen
Refactor dataframe contract helper script into package
Latest commit 58927fa Aug 27, 2019
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readme.md

Seldon Core API

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

  • Deploy machine learning models in the cloud or on-premise.
  • Get metrics and ensure proper governance and compliance for your running machine learning models.
  • Create powerful inference graphs made up of multiple components.
  • Provide a consistent serving layer for models built using heterogeneous ML toolkits.

So Why Seldon?

  • Let's you focus on your model by making it easy to serve on kubernetes
  • The same workflow and base API for a range of toolkits such as scikit-learn, tensorflow and R
  • Out of the box best-practices for logging, tracing and base metrics, applicable to all models across toolkits
  • Support for deployment strategies such as running A/B test and canaries
  • Inferences graphs for microservice-based serving strategies such as multi-armed bandits or pre-processing

Documentation

Documentation can be found here.

Community

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