@darshan-majithiya had opened #15429 to add the hyperparameter tuning PR but it's gone stale. I'm adding this issue to see if they want to pick it back up, or if not, if someone wants to pick up where they left off in the spirit of open source 😄 
airflow/providers/google/cloud/example_dags/example_mlengine.py
if summary['val'] < 0:
            raise ValueError(f'Too low val<0; summary={summary}')
        if summary['count'] != 20:
            raise ValueError(f'Invalid value val != 20; summary={summary}')
        return summary
    
    
    evaluate_prediction, evaluate_summary, evaluate_validation = mlengine_operator_utils.create_evaluate_ops(
        task_prefix="evaluate-ops",
        data_format="TEXT",
        input_paths=[PREDICTION_INPUT],
        prediction_path=PREDICTION_OUTPUT,
        metric_fn_and_keys=get_metric_fn_and_keys(),
        validate_fn=validate_err_and_count,
        batch_prediction_job_id="evaluate-ops-{{ ts_nodash }}-{{ params.model_name }}",
        project_id=PROJECT_ID,
        region="us-central1",
        dataflow_options={
            'project': PROJECT_ID,
            'tempLocation': SUMMARY_TMP,
            'stagingLocation': SUMMARY_STAGING,
        },
        model_name=MODEL_NAME,
        version_name="v1",
        py_interpreter="python3",
    )
    
    create_model >> create_version >> evaluate_prediction
    evaluate_validation >> delete_version