Model

A Model is the core atomic building block. It specifies a machine learning artifact that will be loaded onto one of the running Servers. A model could be a standard machine learning inference component such as

• a Tensorflow model, PyTorch model or SKLearn model.

• an inference transformation component such as a SKLearn pipeline or a piece of custom python logic. a monitoring component such as an outlier detector or drift detector.

• An alibi-explain model explainer

An example is shown below for a SKLearn model for iris classification:

https://github.com/SeldonIO/seldon-core/blob/v2/samples/models/sklearn-iris-gs.yaml

apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: iris
spec:
  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.6.0/iris-sklearn"
  requirements:
  - sklearn
  memory: 100Ki

# samples/models/sklearn-iris-gs.yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
  name: iris
spec:
  storageUri: "gs://seldon-models/scv2/samples/mlserver_1.5.0/iris-sklearn"
  requirements:
  - sklearn
  memory: 100Ki

Its Kubernetes spec has two core requirements

• A storageUri specifying the location of the artifact. This can be any rclone URI specification.

• A requirements list which provides tags that need to be matched by the Server that can run this artifact type. By default when you install Seldon we provide a set of Servers that cover a range of artifact types.