diff --git a/tests/integ/test_chainer_train.py b/tests/integ/test_chainer_train.py
index a4afe785c2..f932bea06a 100644
--- a/tests/integ/test_chainer_train.py
+++ b/tests/integ/test_chainer_train.py
@@ -66,7 +66,7 @@ def test_training_with_additional_hyperparameters(sagemaker_session, chainer_ful
 def test_attach_deploy(chainer_training_job, sagemaker_session):
     endpoint_name = 'test-chainer-attach-deploy-{}'.format(sagemaker_timestamp())
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = Chainer.attach(chainer_training_job, sagemaker_session=sagemaker_session)
         predictor = estimator.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
         _predict_and_assert(predictor)
@@ -74,7 +74,7 @@ def test_attach_deploy(chainer_training_job, sagemaker_session):
 
 def test_deploy_model(chainer_training_job, sagemaker_session):
     endpoint_name = 'test-chainer-deploy-model-{}'.format(sagemaker_timestamp())
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=chainer_training_job)
         model_data = desc['ModelArtifacts']['S3ModelArtifacts']
         script_path = os.path.join(DATA_DIR, 'chainer_mnist', 'mnist.py')
@@ -93,7 +93,7 @@ def test_async_fit(sagemaker_session):
         print("Waiting to re-attach to the training job: %s" % training_job_name)
         time.sleep(20)
 
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=35):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         print("Re-attaching now to: %s" % training_job_name)
         estimator = Chainer.attach(training_job_name=training_job_name, sagemaker_session=sagemaker_session)
         predictor = estimator.deploy(1, "ml.c4.xlarge", endpoint_name=endpoint_name)
diff --git a/tests/integ/test_pytorch_train.py b/tests/integ/test_pytorch_train.py
index cb46fc8fd0..3ef5f26bca 100644
--- a/tests/integ/test_pytorch_train.py
+++ b/tests/integ/test_pytorch_train.py
@@ -41,7 +41,7 @@ def fixture_training_job(sagemaker_session, pytorch_full_version):
 def test_sync_fit_deploy(pytorch_training_job, sagemaker_session):
     # TODO: add tests against local mode when it's ready to be used
     endpoint_name = 'test-pytorch-sync-fit-attach-deploy{}'.format(sagemaker_timestamp())
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         estimator = PyTorch.attach(pytorch_training_job, sagemaker_session=sagemaker_session)
         predictor = estimator.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
         data = numpy.zeros(shape=(1, 1, 28, 28), dtype=numpy.float32)
diff --git a/tests/integ/test_randomcutforest.py b/tests/integ/test_randomcutforest.py
index a749af985c..30624695fd 100644
--- a/tests/integ/test_randomcutforest.py
+++ b/tests/integ/test_randomcutforest.py
@@ -34,7 +34,7 @@ def test_randomcutforest(sagemaker_session):
         rcf.fit(rcf.record_set(train_input))
 
     endpoint_name = name_from_base('randomcutforest')
-    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         model = RandomCutForestModel(rcf.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
         predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
 
diff --git a/tests/integ/test_tuner.py b/tests/integ/test_tuner.py
index e20d238f8d..f8101ff6f9 100644
--- a/tests/integ/test_tuner.py
+++ b/tests/integ/test_tuner.py
@@ -344,7 +344,7 @@ def test_attach_tuning_pytorch(sagemaker_session):
 
     attached_tuner = HyperparameterTuner.attach(tuning_job_name, sagemaker_session=sagemaker_session)
     best_training_job = tuner.best_training_job()
-    with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session, minutes=20):
+    with timeout_and_delete_endpoint_by_name(best_training_job, sagemaker_session):
         predictor = attached_tuner.deploy(1, 'ml.c4.xlarge')
         data = np.zeros(shape=(1, 1, 28, 28), dtype=np.float32)
         predictor.predict(data)
diff --git a/tests/integ/timeout.py b/tests/integ/timeout.py
index 7c5d43e357..43bd6f7691 100644
--- a/tests/integ/timeout.py
+++ b/tests/integ/timeout.py
@@ -60,7 +60,7 @@ def handler(signum, frame):
 
 
 @contextmanager
-def timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, seconds=0, minutes=35, hours=0):
+def timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session, seconds=0, minutes=45, hours=0):
     with timeout(seconds=seconds, minutes=minutes, hours=hours) as t:
         no_errors = False
         try: