diff --git a/src/main/python/tests/algorithms/test_gmm.py b/src/main/python/tests/algorithms/test_gmm.py
index 066c957b4ac..10a561fe0e9 100644
--- a/src/main/python/tests/algorithms/test_gmm.py
+++ b/src/main/python/tests/algorithms/test_gmm.py
@@ -48,11 +48,11 @@ def test_lm_simple(self):
 
         n_gaussian = 4
 
-        [_, _, _, _, mu, precision_cholesky, wight] = gmm(
+        [_, _, _, _, mu, precision_cholesky, weight] = gmm(
             features, False, n_components=n_gaussian, seed=10)
 
         [_, pp] = gmmPredict(
-            test, wight, mu, precision_cholesky, model=self.sds.scalar("VVV"))
+            test, weight, mu, precision_cholesky, model=self.sds.scalar("VVV"))
 
         outliers = pp.max(axis=1) < 0.99
         ret = outliers.compute()
diff --git a/src/main/python/tests/algorithms/test_gmm_train_predict.py b/src/main/python/tests/algorithms/test_gmm_train_predict.py
new file mode 100644
index 00000000000..5bd53a8907a
--- /dev/null
+++ b/src/main/python/tests/algorithms/test_gmm_train_predict.py
@@ -0,0 +1,78 @@
+# -------------------------------------------------------------
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+# -------------------------------------------------------------
+
+import shutil
+import unittest
+
+from systemds.context import SystemDSContext
+from systemds.operator.algorithm import gmm, gmmPredict
+
+
+class TestGMM(unittest.TestCase):
+
+    model_dir: str =  "tests/algorithm/readwrite/"
+    model_path: str = model_dir + "model"
+
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.model_dir)
+
+    def test_train_and_predict(self):
+        self.train()
+        self.predict()
+
+    def train(self):
+        with SystemDSContext() as sds_train:
+            a = sds_train.rand(500, 10, -100, 100, pdf="normal", seed=10)
+            features = a  # training data all not outliers
+
+            n_gaussian = 4
+
+            [_, _, _, _, mu, precision_cholesky, weight] = gmm(
+                features, False, n_components=n_gaussian, seed=10)
+
+            model = sds_train.list(mu, precision_cholesky, weight)
+            model.write(self.model_path).compute()
+
+    def predict(self):
+        with SystemDSContext() as sds_predict:
+            model = sds_predict.read(self.model_path)
+            mu = model[1].as_matrix()
+            precision_cholesky = model[2].as_matrix()
+            weight = model[3].as_matrix()
+            notOutliers = sds_predict.rand(
+                10, 10, -1, 1,  seed=10)  # inside a
+            outliers = sds_predict.rand(
+                10, 10, 1150, 1200, seed=10)  # outliers
+
+            test = outliers.rbind(notOutliers)  # testing data half outliers
+
+            [_, pp] = gmmPredict(
+                test, weight, mu, precision_cholesky, model=sds_predict.scalar("VVV"))
+
+            outliers = pp.max(axis=1) < 0.99
+            ret = outliers.compute()
+
+            self.assertTrue(ret.sum() == 10)
+
+
+if __name__ == "__main__":
+    unittest.main(exit=False)