def METHOD_NAME(container_type: Optional[Union[str, 'ContainerType']] = None,
                               deployment_name: Optional[str] = None,
                               endpoint_name: Optional[str] = None,
                               resource_group_name: Optional[str] = None,
                               tail: Optional[int] = None,
                               workspace_name: Optional[str] = None,
                               opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetOnlineDeploymentLogsResult:
    """
    Use this data source to access information about an existing resource.
    :param Union[str, 'ContainerType'] container_type: The type of container to retrieve logs from.
    :param str deployment_name: The name and identifier for the endpoint.
    :param str endpoint_name: Inference endpoint name.
    :param str resource_group_name: The name of the resource group. The name is case insensitive.
    :param int tail: The maximum number of lines to tail.
    :param str workspace_name: Name of Azure Machine Learning workspace.
    """
    __args__ = dict()
    __args__['containerType'] = container_type
    __args__['deploymentName'] = deployment_name
    __args__['endpointName'] = endpoint_name
    __args__['resourceGroupName'] = resource_group_name
    __args__['tail'] = tail
    __args__['workspaceName'] = workspace_name
    opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts)
    __ret__ = pulumi.runtime.invoke('azure-native:machinelearningservices/v20230401:getOnlineDeploymentLogs', __args__, opts=opts, typ=GetOnlineDeploymentLogsResult).value
    return AwaitableGetOnlineDeploymentLogsResult(
        content=pulumi.get(__ret__, 'content'))