Models can be deployed using Verta's scalable, configurable endpoints.
Endpoints are easy to create through either the client:
endpoint = client.create_endpoint("classify-income")
or the webapp:
endpoint.update()can also accept an experiment run rather than a model version. In addition, the
waitparameter can be set to
Trueto wait for the deployed model to be ready before executing the next line of code.
Once an endpoint is deployed, predictions can be made against it either through the Python client:
deployed_model = endpoint.get_deployed_model()
for row in data:
or via a REST call:
curl -H "Access-token: 12345678-abcd-1234-abcd-123456789012" -X POST https://app.verta.ai/api/v1/predict/classify-income -d '[1,2,3,4,5,6]' -H "Content-Type: application/json"