Seldon Core Release 0.4.0¶
A summary of the main contributions to the Seldon Core release 0.4.0.
Prepackaged Model Servers¶
Seldon provides several prepacked servers you can use to deploy trained models:
For these servers you only need the location of the saved model in a local filestore, Google bucket or S3 bucket. An example manifest with an sklearn server is shown below:
apiVersion: machinelearning.seldon.io/v1alpha2 kind: SeldonDeployment metadata: name: sklearn spec: name: iris predictors: - graph: children:  implementation: SKLEARN_SERVER modelUri: gs://seldon-models/sklearn/iris name: classifier name: default replicas: 1
modelUri specifies the bucket containing the saved model, in this case
modeluri supports the following three object storage providers:
Google Cloud Storage (using
Azure Blob storage (using
Gunicorn Alpha Feature¶
We have provided an early alpha release for the python language wrapper to run under gunicorn rather than Flask. For further details see our gunicorn documentation.
We have a kustomize resource you can use and extend for your own particular setup for installing Seldon Core.
More Example Integrations¶
Our range of example has expanded to include: