ferruzzi commented on code in PR #38693:
URL: https://github.com/apache/airflow/pull/38693#discussion_r1550254242


##########
airflow/providers/amazon/aws/operators/bedrock.py:
##########
@@ -91,3 +96,150 @@ def execute(self, context: Context) -> dict[str, str | int]:
         self.log.info("Bedrock %s prompt: %s", self.model_id, self.input_data)
         self.log.info("Bedrock model response: %s", response_body)
         return response_body
+
+
+class BedrockCustomizeModelOperator(AwsBaseOperator[BedrockHook]):
+    """
+    Create a fine-tuning job to customize a base model.
+
+    .. seealso::
+        For more information on how to use this operator, take a look at the 
guide:
+        :ref:`howto/operator:BedrockCustomizeModelOperator`
+
+    :param job_name: A unique name for the fine-tuning job.
+    :param custom_model_name: A name for the custom model being created.
+    :param role_arn: The Amazon Resource Name (ARN) of an IAM role that Amazon 
Bedrock can assume
+        to perform tasks on your behalf.
+    :param base_model_id: Name of the base model.
+    :param training_data_uri: The S3 URI where the training data is stored.
+    :param output_data_uri: The S3 URI where the output data is stored.
+    :param hyperparameters: Parameters related to tuning the model.
+    :param check_if_job_exists: If set to true, operator will check whether a 
model customization
+        job already exists for the name in the config. (Default: True)
+    :param action_if_job_exists: Behavior if the job name already exists. 
Options are "timestamp" (default),
+        and "fail"
+    :param customization_job_kwargs: Any optional parameters to pass to the 
API.
+
+    :param wait_for_completion: Whether to wait for cluster to stop. (default: 
True)
+    :param waiter_delay: Time in seconds to wait between status checks.
+    :param waiter_max_attempts: Maximum number of attempts to check for job 
completion.
+    :param deferrable: If True, the operator will wait asynchronously for the 
cluster to stop.
+        This implies waiting for completion. This mode requires aiobotocore 
module to be installed.
+        (default: False)
+    :param aws_conn_id: The Airflow connection used for AWS credentials.
+        If this is ``None`` or empty then the default boto3 behaviour is used. 
If
+        running Airflow in a distributed manner and aws_conn_id is None or
+        empty, then default boto3 configuration would be used (and must be
+        maintained on each worker node).
+    :param region_name: AWS region_name. If not specified then the default 
boto3 behaviour is used.
+    :param verify: Whether or not to verify SSL certificates. See:
+        
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html
+    :param botocore_config: Configuration dictionary (key-values) for botocore 
client. See:
+        
https://botocore.amazonaws.com/v1/documentation/api/latest/reference/config.html
+    """
+
+    aws_hook_class = BedrockHook
+    template_fields: Sequence[str] = aws_template_fields(
+        "job_name",
+        "custom_model_name",
+        "role_arn",
+        "base_model_id",
+        "hyperparameters",
+        "check_if_job_exists",
+        "action_if_job_exists",
+        "customization_job_kwargs",
+    )
+
+    def __init__(
+        self,
+        job_name: str,
+        custom_model_name: str,
+        role_arn: str,
+        base_model_id: str,
+        training_data_uri: str,
+        output_data_uri: str,
+        hyperparameters: dict[str, str],
+        check_if_job_exists: bool = True,
+        action_if_job_exists: str = "timestamp",
+        customization_job_kwargs: dict[str, Any] | None = None,
+        wait_for_completion: bool = True,
+        waiter_delay: int = 120,
+        waiter_max_attempts: int = 75,
+        deferrable: bool = conf.getboolean("operators", "default_deferrable", 
fallback=False),
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.wait_for_completion = wait_for_completion
+        self.waiter_delay = waiter_delay
+        self.waiter_max_attempts = waiter_max_attempts
+        self.deferrable = deferrable
+
+        self.job_name = job_name
+        self.custom_model_name = custom_model_name
+        self.role_arn = role_arn
+        self.base_model_id = base_model_id
+        self.training_data_config = {"s3Uri": training_data_uri}
+        self.output_data_config = {"s3Uri": output_data_uri}
+        self.hyperparameters = hyperparameters
+        self.check_if_job_exists = check_if_job_exists
+        self.customization_job_kwargs = customization_job_kwargs or {}
+        if action_if_job_exists in {"timestamp", "fail"}:
+            self.action_if_job_exists = action_if_job_exists
+        else:
+            raise AirflowException(
+                f"Argument action_if_job_exists accepts only 'timestamp', and 
'fail'. \
+                Provided value: '{action_if_job_exists}."
+            )

Review Comment:
   done



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