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  """Model deployers are stack components responsible for online model serving.
  
  Online serving is the process of hosting and loading machine-learning models as
  part of a managed web service and providing access to the models through an API
  endpoint like HTTP or GRPC. Once deployed, you can send inference requests
  to the model through the web service's API and receive fast, low-latency
  responses.
  
  Add a model deployer to your ZenML stack to be able to implement continuous
  model deployment pipelines that train models and continuously deploy them to a
  model prediction web service.
  
  When present in a stack, the model deployer also acts as a registry for models
  that are served with ZenML. You can use the model deployer to list all models
  that are currently deployed for online inference or filtered according
  to a particular pipeline run or step, or to suspend, resume or delete
  an external model server managed through ZenML.
  """
  
  from zenml.model_deployers.base_model_deployer import (
      BaseModelDeployer,
      BaseModelDeployerFlavor,
  )
  
  __all__ = [
      "BaseModelDeployer",
      "BaseModelDeployerFlavor",
  ]
  