def submit_experiment_to_azureml(test, test_folder, test_markers, junitxml,
                                 run_config, experiment):

    """
    Submitting the experiment to AzureML actually runs the script.

    Args:
        test         (str) - pytest script, folder/test
                             such as ./tests/ci/run_pytest.py
        test_folder  (str) - folder where tests to run are stored,
                             like ./tests/unit
        test_markers (str) - test markers used by pytest
                             "not notebooks and not spark and not gpu"
        junitxml     (str) - file of output summary of tests run
                             note "--junitxml" is required as part
                             of the string
                             Example: "--junitxml reports/test-unit.xml"
        run_config - environment configuration
        experiment - instance of an Experiment, a collection of
                     trials where each trial is a run.
    Return:
          run : AzureML run or trial
    """

    logger.debug('submit: testfolder {}'.format(test_folder))
    logger.debug('junitxml: {}'.format(junitxml))
    project_folder = "."

    script_run_config = ScriptRunConfig(source_directory=project_folder,
                                        script=test,
                                        run_config=run_config,
                                        arguments=["--testfolder",
                                                   test_folder,
                                                   "--testmarkers",
                                                   test_markers,
                                                   "--xmlname",
                                                   junitxml]
                                        )
    run = experiment.submit(script_run_config)
    # waits only for configuration to complete
    run.wait_for_completion(show_output=True, wait_post_processing=True)

    # test logs can also be found on azure
    # go to azure portal to see log in azure ws and look for experiment name
    # and look for individual run
    logger.debug('files {}'.format(run.get_file_names))

    return run