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$ zenml model-deployer models get-url 8cbe671b-9fce-4394-a051-68e001f92765

Prediction URL of Served Model 8cbe671b-9fce-4394-a051-68e001f92765 is:

http://abb84c444c7804aa98fc8c097896479d-377673393.us-east-1.elb.amazonaws.com/seldon/zenml-workloads/zenml-8cbe67

1b-9fce-4394-a051-68e001f92765/api/v0.1/predictions

$ zenml model-deployer models delete 8cbe671b-9fce-4394-a051-68e001f92765

In Python, you can alternatively discover the prediction URL of a deployed model by inspecting the metadata of the step that deployed the model:

from zenml.client import Client

pipeline_run = Client().get_pipeline_run("<PIPELINE_RUN_NAME>")

deployer_step = pipeline_run.steps["<NAME_OF_MODEL_DEPLOYER_STEP>"]

deployed_model_url = deployer_step.run_metadata["deployed_model_url"].value

The ZenML integrations that provide Model Deployer stack components also include standard pipeline steps that can directly be inserted into any pipeline to achieve a continuous model deployment workflow. These steps take care of all the aspects of continuously deploying models to an external server and saving the Service configuration into the Artifact Store, where they can be loaded at a later time and re-create the initial conditions used to serve a particular model.

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Last updated 19 days ago