此问题是因为无法在自动配置资源 (automatic_resources) 方式下部署模型所导致的。要解决此问题，您可以通过手动指定所需资源量来更改为手动配置资源，并使用以下代码示例进行部署：

from google.cloud import automl

model_id = 'YOUR_MODEL_ID'
project_id = 'YOUR_PROJECT_ID'
region = 'YOUR_REGION'

client = automl.AutoMlClient()

# Get the full path of the model
model_full_id = client.model_path(project_id, region, model_id)

# Specify the desired deployment node
model_deployment_metadata = automl.types.ModelDeploymentMetadata(
    node_count=1,
    cpu_cores_per_node=1,
    memory_gb_per_node=2
)
deployment = automl.types.ModelDeploymentMetadata(
    image_uri="gcr.io/cloud-ml-service/disposable-tfserving-service:latest")

# Deploy the model
response = client.deploy_model(
    model_full_id=model_full_id,
    deployment_metadata=model_deployment_metadata,
    deployment=deployment,
)

print("Model deployed. {}".format(response.deployed_model_id))

在上面的示例代码中，您需要将 YOUR_MODEL_ID 替换为模型 ID，YOUR_PROJECT_ID 替换为项目 ID，YOUR_REGION 替换为部署区域。同时通过手动配置资源来确保成功部署。