The deployer is responsible for serving a trained model to an endpoint. It can be added to a
There are some standard deployers built-in to ZenML for common deployment scenarios.
Deploys the model directly to a Google Cloud AI Platform end-point.
from zenml.core.steps.deployer.gcaip_deployer import GCAIPDeployer pipeline.add_deployment(GCAIPDeployer( project_id='project_id', model_name='model_name', ))
Currently, the GCAIPDeployer only works with Trainers fully implementing the
TFBaseTrainerStep interface. An example is the standard
Create custom deployer¶
The mechanism to create a custom Deployer will be published in more detail soon in this space. However, the details of this are currently being worked out and will be made available in future releases.
Downloading a trained model¶
The model will be present in the
TrainerStep artifacts directory.
You can retrieve this URI directly from a pipeline by executing: