train_url = 'http://0.0.0.0:8080/train_pipeline'
train_files = {'raw_data': open('data/data_train.json', 'rb'),
               'labels'  : open('data/label_train.json', 'rb'),
               'params'  : open('parameters/train_parameters_model2.yml', 'rb')}

# post request to train pipeline
r_train = requests.post(train_url, files=train_files)
result_df = json.loads(r_train.json())

{'featureEngParams': {'default_fc_parameters': "['median', 'minimum', 'standard_deviation', 
                                                 'sum_values', 'variance', 'maximum', 
                                                 'length', 'mean']",
                      'impute_function': 'impute',
                      ...},
 'mean_cv_accuracy': 0.865,
 'mean_cv_roc_auc': 0.932,
 'modelId': 1,
 'modelType': "Pipeline(steps=[('stackingestimator', StackingEstimator(estimator=LinearSVC(...))),
                               ('logisticregression', LogisticRegressionClassifier(solver='liblinear',...))])"
 'trainShape': [1647, 8],
 'trainTime': 1.953}

serve_url = 'http://0.0.0.0:8080/serve_prediction'
test_files = {'raw_data': open('data/data_test.json', 'rb'),
              'params' : open('parameters/test_parameters_model2.yml', 'rb')}

# post request to serve predictions from trained pipeline
r_test  = requests.post(serve_url, files=test_files)
result = pd.read_json(r_test.json()).set_index('id')

r = requests.get('http://0.0.0.0:8080/models')
pipelines = json.loads(r.json())

{'1':
    {'mean_cv_accuracy': 0.873,
     'modelType': "RandomForestClassifier(...),
     ...},
 '2':
    {'mean_cv_accuracy': 0.895,
     'modelType': "GradientBoostingClassifier(...),
     ...},
 '3':
    {'mean_cv_accuracy': 0.859,
     'modelType': "LogisticRegressionClassifier(...),
     ...},
...}