class_docstr: Base class exposing Python model deployment APIs.
  
  Plugin implementors should define target-specific deployment logic via a subclass of
  ``BaseDeploymentClient`` within the plugin module, and customize method docstrings with
  target-specific information.
  
  .. Note::
      Subclasses should raise :py:class:`mlflow.exceptions.MlflowException` in error cases (e.g.
      on failure to deploy a model).
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docstr: Deploy a model to the specified target. By default, this method should block until
  deployment completes (i.e. until it's possible to perform inference with the deployment).
  In the case of conflicts (e.g. if it's not possible to create the specified deployment
  without due to conflict with an existing deployment), raises a
  :py:class:`mlflow.exceptions.MlflowException`. See target-specific plugin documentation
  for additional detail on support for asynchronous deployment and other configuration.
  
  Args:
      name: Unique name to use for deployment. If another deployment exists with the same
          name, raises a :py:class:`mlflow.exceptions.MlflowException`
      model_uri: URI of model to deploy
      flavor: (optional) Model flavor to deploy. If unspecified, a default flavor
          will be chosen.
      config: (optional) Dict containing updated target-specific configuration for the
          deployment
      endpoint: (optional) Endpoint to create the deployment under. May not be supported
          by all targets
  
  Returns:
      Dict corresponding to created deployment, which must contain the 'name' key.
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code: def create_deployment(
      self, name, model_uri, flavor=None, config=None, endpoint=None
  ):
      """
      Deploy a model to the specified target. By default, this method should block until
      deployment completes (i.e. until it's possible to perform inference with the deployment).
      In the case of conflicts (e.g. if it's not possible to create the specified deployment
      without due to conflict with an existing deployment), raises a
      :py:class:`mlflow.exceptions.MlflowException`. See target-specific plugin documentation
      for additional detail on support for asynchronous deployment and other configuration.
  
      Args:
          name: Unique name to use for deployment. If another deployment exists with the same
              name, raises a :py:class:`mlflow.exceptions.MlflowException`
          model_uri: URI of model to deploy
          flavor: (optional) Model flavor to deploy. If unspecified, a default flavor
              will be chosen.
          config: (optional) Dict containing updated target-specific configuration for the
              deployment
          endpoint: (optional) Endpoint to create the deployment under. May not be supported
              by all targets
  
      Returns:
          Dict corresponding to created deployment, which must contain the 'name' key.
  
      """
      pass
  
file_path: mlflow/mlflow/deployments/base.py
imports: import abc
  from mlflow.exceptions import MlflowException
  from mlflow.utils.annotations import developer_stable
label_desc: Use image node_id 2 for calling the BaseDeploymentClient obj's underlying member method code with example usage: obj.create_deployment(name, model_uri, flavor, config, endpoint) without return types