diff --git a/src/sagemaker/amazon/factorization_machines.py b/src/sagemaker/amazon/factorization_machines.py
index 7544a3d5c7..48bed830cb 100644
--- a/src/sagemaker/amazon/factorization_machines.py
+++ b/src/sagemaker/amazon/factorization_machines.py
@@ -277,13 +277,20 @@ class FactorizationMachinesPredictor(Predictor):
     to fit the model this Predictor performs inference on.
 
     :meth:`predict()` returns a list of
-    :class:`~sagemaker.amazon.record_pb2.Record` objects, one for each row in
+    :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default
+    recordio-protobuf ``deserializer`` is used), one for each row in
     the input ``ndarray``. The prediction is stored in the ``"score"`` key of
     the ``Record.label`` field. Please refer to the formats details described:
     https://docs.aws.amazon.com/sagemaker/latest/dg/fm-in-formats.html
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=RecordSerializer(),
+        deserializer=RecordDeserializer(),
+    ):
         """
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -292,12 +299,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 object, used for SageMaker interactions (default: None). If not
                 specified, one is created using the default AWS configuration
                 chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to x-recordio-protobuf format.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses responses from x-recordio-protobuf format.
         """
         super(FactorizationMachinesPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=RecordSerializer(),
-            deserializer=RecordDeserializer(),
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/amazon/ipinsights.py b/src/sagemaker/amazon/ipinsights.py
index 8f4c2964e0..34a0173e2f 100644
--- a/src/sagemaker/amazon/ipinsights.py
+++ b/src/sagemaker/amazon/ipinsights.py
@@ -191,7 +191,13 @@ class IPInsightsPredictor(Predictor):
     second column should contain the IPv4 address in dot notation.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=CSVSerializer(),
+        deserializer=JSONDeserializer(),
+    ):
         """
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -200,12 +206,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 object, used for SageMaker interactions (default: None). If not
                 specified, one is created using the default AWS configuration
                 chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to text/csv.
+            deserializer (callable): Optional. Default parses JSON responses
+                using ``json.load(...)``.
         """
         super(IPInsightsPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=CSVSerializer(),
-            deserializer=JSONDeserializer(),
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/amazon/kmeans.py b/src/sagemaker/amazon/kmeans.py
index 2b405b6f62..59d6fe2116 100644
--- a/src/sagemaker/amazon/kmeans.py
+++ b/src/sagemaker/amazon/kmeans.py
@@ -210,12 +210,19 @@ class KMeansPredictor(Predictor):
     to fit the model this Predictor performs inference on.
 
     ``predict()`` returns a list of
-    :class:`~sagemaker.amazon.record_pb2.Record` objects, one for each row in
+    :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default
+    recordio-protobuf ``deserializer`` is used), one for each row in
     the input ``ndarray``. The nearest cluster is stored in the
     ``closest_cluster`` key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=RecordSerializer(),
+        deserializer=RecordDeserializer(),
+    ):
         """
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -224,12 +231,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 object, used for SageMaker interactions (default: None). If not
                 specified, one is created using the default AWS configuration
                 chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to x-recordio-protobuf format.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses responses from x-recordio-protobuf format.
         """
         super(KMeansPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=RecordSerializer(),
-            deserializer=RecordDeserializer(),
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/amazon/knn.py b/src/sagemaker/amazon/knn.py
index 1f1822c8f8..f5af87e377 100644
--- a/src/sagemaker/amazon/knn.py
+++ b/src/sagemaker/amazon/knn.py
@@ -199,12 +199,19 @@ class KNNPredictor(Predictor):
     to fit the model this Predictor performs inference on.
 
     :func:`predict` returns a list of
-    :class:`~sagemaker.amazon.record_pb2.Record` objects, one for each row in
+    :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default
+    recordio-protobuf ``deserializer`` is used), one for each row in
     the input ``ndarray``. The prediction is stored in the ``"predicted_label"``
     key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=RecordSerializer(),
+        deserializer=RecordDeserializer(),
+    ):
         """
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -213,12 +220,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 object, used for SageMaker interactions (default: None). If not
                 specified, one is created using the default AWS configuration
                 chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to x-recordio-protobuf format.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses responses from x-recordio-protobuf format.
         """
         super(KNNPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=RecordSerializer(),
-            deserializer=RecordDeserializer(),
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/amazon/lda.py b/src/sagemaker/amazon/lda.py
index 4740f53085..8c459b3aac 100644
--- a/src/sagemaker/amazon/lda.py
+++ b/src/sagemaker/amazon/lda.py
@@ -183,12 +183,19 @@ class LDAPredictor(Predictor):
     to fit the model this Predictor performs inference on.
 
     :meth:`predict()` returns a list of
-    :class:`~sagemaker.amazon.record_pb2.Record` objects, one for each row in
+    :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default
+    recordio-protobuf ``deserializer`` is used), one for each row in
     the input ``ndarray``. The lower dimension vector result is stored in the
     ``projection`` key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=RecordSerializer(),
+        deserializer=RecordDeserializer(),
+    ):
         """
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -197,12 +204,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 object, used for SageMaker interactions (default: None). If not
                 specified, one is created using the default AWS configuration
                 chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to x-recordio-protobuf format.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses responses from x-recordio-protobuf format.
         """
         super(LDAPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=RecordSerializer(),
-            deserializer=RecordDeserializer(),
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/amazon/linear_learner.py b/src/sagemaker/amazon/linear_learner.py
index e1eec87a03..a7c798c188 100644
--- a/src/sagemaker/amazon/linear_learner.py
+++ b/src/sagemaker/amazon/linear_learner.py
@@ -444,12 +444,19 @@ class LinearLearnerPredictor(Predictor):
     to fit the model this Predictor performs inference on.
 
     :func:`predict` returns a list of
-    :class:`~sagemaker.amazon.record_pb2.Record` objects, one for each row in
+    :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default
+    recordio-protobuf ``deserializer`` is used), one for each row in
     the input ``ndarray``. The prediction is stored in the ``"predicted_label"``
     key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=RecordSerializer(),
+        deserializer=RecordDeserializer(),
+    ):
         """
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -458,12 +465,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 object, used for SageMaker interactions (default: None). If not
                 specified, one is created using the default AWS configuration
                 chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to x-recordio-protobuf format.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses responses from x-recordio-protobuf format.
         """
         super(LinearLearnerPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=RecordSerializer(),
-            deserializer=RecordDeserializer(),
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/amazon/ntm.py b/src/sagemaker/amazon/ntm.py
index 9dcec3bfe5..d24a1aee2f 100644
--- a/src/sagemaker/amazon/ntm.py
+++ b/src/sagemaker/amazon/ntm.py
@@ -212,12 +212,19 @@ class NTMPredictor(Predictor):
     to fit the model this Predictor performs inference on.
 
     :meth:`predict()` returns a list of
-    :class:`~sagemaker.amazon.record_pb2.Record` objects, one for each row in
+    :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default
+    recordio-protobuf ``deserializer`` is used), one for each row in
     the input ``ndarray``. The lower dimension vector result is stored in the
     ``projection`` key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=RecordSerializer(),
+        deserializer=RecordDeserializer(),
+    ):
         """
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -226,12 +233,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 object, used for SageMaker interactions (default: None). If not
                 specified, one is created using the default AWS configuration
                 chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to x-recordio-protobuf format.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses responses from x-recordio-protobuf format.
         """
         super(NTMPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=RecordSerializer(),
-            deserializer=RecordDeserializer(),
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/amazon/pca.py b/src/sagemaker/amazon/pca.py
index 2d38dda3f7..7c11e05494 100644
--- a/src/sagemaker/amazon/pca.py
+++ b/src/sagemaker/amazon/pca.py
@@ -193,12 +193,19 @@ class PCAPredictor(Predictor):
     to fit the model this Predictor performs inference on.
 
     :meth:`predict()` returns a list of
-    :class:`~sagemaker.amazon.record_pb2.Record` objects, one for each row in
+    :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default
+    recordio-protobuf ``deserializer`` is used), one for each row in
     the input ``ndarray``. The lower dimension vector result is stored in the
     ``projection`` key of the ``Record.label`` field.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=RecordSerializer(),
+        deserializer=RecordDeserializer(),
+    ):
         """
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -207,12 +214,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 object, used for SageMaker interactions (default: None). If not
                 specified, one is created using the default AWS configuration
                 chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to x-recordio-protobuf format.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses responses from x-recordio-protobuf format.
         """
         super(PCAPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=RecordSerializer(),
-            deserializer=RecordDeserializer(),
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/amazon/randomcutforest.py b/src/sagemaker/amazon/randomcutforest.py
index ea7d824355..ce5b79178f 100644
--- a/src/sagemaker/amazon/randomcutforest.py
+++ b/src/sagemaker/amazon/randomcutforest.py
@@ -171,12 +171,19 @@ class RandomCutForestPredictor(Predictor):
     to fit the model this Predictor performs inference on.
 
     :meth:`predict()` returns a list of
-    :class:`~sagemaker.amazon.record_pb2.Record` objects, one for each row in
+    :class:`~sagemaker.amazon.record_pb2.Record` objects (assuming the default
+    recordio-protobuf ``deserializer`` is used), one for each row in
     the input. Each row's score is stored in the key ``score`` of the
     ``Record.label`` field.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=RecordSerializer(),
+        deserializer=RecordDeserializer(),
+    ):
         """
         Args:
             endpoint_name (str): Name of the Amazon SageMaker endpoint to which
@@ -185,12 +192,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 object, used for SageMaker interactions (default: None). If not
                 specified, one is created using the default AWS configuration
                 chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to x-recordio-protobuf format.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses responses from x-recordio-protobuf format.
         """
         super(RandomCutForestPredictor, self).__init__(
             endpoint_name,
             sagemaker_session,
-            serializer=RecordSerializer(),
-            deserializer=RecordDeserializer(),
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/chainer/model.py b/src/sagemaker/chainer/model.py
index 48ca779257..d7f6debfbe 100644
--- a/src/sagemaker/chainer/model.py
+++ b/src/sagemaker/chainer/model.py
@@ -38,7 +38,13 @@ class ChainerPredictor(Predictor):
     multidimensional tensors for Chainer inference.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=NumpySerializer(),
+        deserializer=NumpyDeserializer(),
+    ):
         """Initialize an ``ChainerPredictor``.
 
         Args:
@@ -48,9 +54,17 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 manages interactions with Amazon SageMaker APIs and any other
                 AWS services needed. If not specified, the estimator creates one
                 using the default AWS configuration chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to .npy format. Handles lists and numpy
+                arrays.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses the response from .npy format to numpy array.
         """
         super(ChainerPredictor, self).__init__(
-            endpoint_name, sagemaker_session, NumpySerializer(), NumpyDeserializer()
+            endpoint_name,
+            sagemaker_session,
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/mxnet/model.py b/src/sagemaker/mxnet/model.py
index d1317c59a1..624e5cfbe1 100644
--- a/src/sagemaker/mxnet/model.py
+++ b/src/sagemaker/mxnet/model.py
@@ -40,7 +40,13 @@ class MXNetPredictor(Predictor):
     multidimensional tensors for MXNet inference.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=JSONSerializer(),
+        deserializer=JSONDeserializer(),
+    ):
         """Initialize an ``MXNetPredictor``.
 
         Args:
@@ -50,9 +56,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 manages interactions with Amazon SageMaker APIs and any other
                 AWS services needed. If not specified, the estimator creates one
                 using the default AWS configuration chain.
+            serializer (callable): Optional. Default serializes input data to
+                json. Handles dicts, lists, and numpy arrays.
+            deserializer (callable): Optional. Default parses the response using
+                ``json.load(...)``.
         """
         super(MXNetPredictor, self).__init__(
-            endpoint_name, sagemaker_session, JSONSerializer(), JSONDeserializer()
+            endpoint_name,
+            sagemaker_session,
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/pytorch/model.py b/src/sagemaker/pytorch/model.py
index b7ba240597..0b752ae9a3 100644
--- a/src/sagemaker/pytorch/model.py
+++ b/src/sagemaker/pytorch/model.py
@@ -39,7 +39,13 @@ class PyTorchPredictor(Predictor):
     multidimensional tensors for PyTorch inference.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=NumpySerializer(),
+        deserializer=NumpyDeserializer(),
+    ):
         """Initialize an ``PyTorchPredictor``.
 
         Args:
@@ -49,9 +55,17 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 manages interactions with Amazon SageMaker APIs and any other
                 AWS services needed. If not specified, the estimator creates one
                 using the default AWS configuration chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to .npy format. Handles lists and numpy
+                arrays.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses the response from .npy format to numpy array.
         """
         super(PyTorchPredictor, self).__init__(
-            endpoint_name, sagemaker_session, NumpySerializer(), NumpyDeserializer()
+            endpoint_name,
+            sagemaker_session,
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/sklearn/model.py b/src/sagemaker/sklearn/model.py
index 2a505fe924..06d28ae14c 100644
--- a/src/sagemaker/sklearn/model.py
+++ b/src/sagemaker/sklearn/model.py
@@ -34,7 +34,13 @@ class SKLearnPredictor(Predictor):
     multidimensional tensors for Scikit-learn inference.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=NumpySerializer(),
+        deserializer=NumpyDeserializer(),
+    ):
         """Initialize an ``SKLearnPredictor``.
 
         Args:
@@ -44,9 +50,17 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 manages interactions with Amazon SageMaker APIs and any other
                 AWS services needed. If not specified, the estimator creates one
                 using the default AWS configuration chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to .npy format. Handles lists and numpy
+                arrays.
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses the response from .npy format to numpy array.
         """
         super(SKLearnPredictor, self).__init__(
-            endpoint_name, sagemaker_session, NumpySerializer(), NumpyDeserializer()
+            endpoint_name,
+            sagemaker_session,
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/src/sagemaker/sparkml/model.py b/src/sagemaker/sparkml/model.py
index 9661d091fd..482f391713 100644
--- a/src/sagemaker/sparkml/model.py
+++ b/src/sagemaker/sparkml/model.py
@@ -31,7 +31,13 @@ class SparkMLPredictor(Predictor):
     list.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None, **kwargs):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=CSVSerializer(),
+        **kwargs,
+    ):
         """Initializes a SparkMLPredictor which should be used with SparkMLModel
         to perform predictions against SparkML models serialized via MLeap. The
         response is returned in text/csv format which is the default response
@@ -43,12 +49,14 @@ def __init__(self, endpoint_name, sagemaker_session=None, **kwargs):
                 manages interactions with Amazon SageMaker APIs and any other
                 AWS services needed. If not specified, the estimator creates one
                 using the default AWS configuration chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to text/csv.
         """
         sagemaker_session = sagemaker_session or Session()
         super(SparkMLPredictor, self).__init__(
             endpoint_name=endpoint_name,
             sagemaker_session=sagemaker_session,
-            serializer=CSVSerializer(),
+            serializer=serializer,
             **kwargs,
         )
 
diff --git a/src/sagemaker/xgboost/model.py b/src/sagemaker/xgboost/model.py
index 400f3e6bcc..536bf75020 100644
--- a/src/sagemaker/xgboost/model.py
+++ b/src/sagemaker/xgboost/model.py
@@ -35,7 +35,13 @@ class XGBoostPredictor(Predictor):
     for XGBoost inference.
     """
 
-    def __init__(self, endpoint_name, sagemaker_session=None):
+    def __init__(
+        self,
+        endpoint_name,
+        sagemaker_session=None,
+        serializer=LibSVMSerializer(),
+        deserializer=CSVDeserializer(),
+    ):
         """Initialize an ``XGBoostPredictor``.
 
         Args:
@@ -44,9 +50,16 @@ def __init__(self, endpoint_name, sagemaker_session=None):
                 interactions with Amazon SageMaker APIs and any other AWS services needed.
                 If not specified, the estimator creates one using the default AWS configuration
                 chain.
+            serializer (sagemaker.serializers.BaseSerializer): Optional. Default
+                serializes input data to LibSVM format
+            deserializer (sagemaker.deserializers.BaseDeserializer): Optional.
+                Default parses the response from text/csv to a Python list.
         """
         super(XGBoostPredictor, self).__init__(
-            endpoint_name, sagemaker_session, LibSVMSerializer(), CSVDeserializer()
+            endpoint_name,
+            sagemaker_session,
+            serializer=serializer,
+            deserializer=deserializer,
         )
 
 
diff --git a/tests/unit/test_chainer.py b/tests/unit/test_chainer.py
index 5679bb5284..d2aaaec8d9 100644
--- a/tests/unit/test_chainer.py
+++ b/tests/unit/test_chainer.py
@@ -383,6 +383,29 @@ def test_model(sagemaker_session, chainer_version, chainer_py_version):
     assert isinstance(predictor, ChainerPredictor)
 
 
+@patch("sagemaker.utils.create_tar_file", MagicMock())
+def test_model_custom_serialization(sagemaker_session, chainer_version, chainer_py_version):
+    model = ChainerModel(
+        "s3://some/data.tar.gz",
+        role=ROLE,
+        entry_point=SCRIPT_PATH,
+        sagemaker_session=sagemaker_session,
+        framework_version=chainer_version,
+        py_version=chainer_py_version,
+    )
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        CPU,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+    assert isinstance(predictor, ChainerPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
+
+
 @patch("sagemaker.fw_utils.tar_and_upload_dir", MagicMock())
 def test_model_prepare_container_def_accelerator_error(
     sagemaker_session, chainer_version, chainer_py_version
diff --git a/tests/unit/test_fm.py b/tests/unit/test_fm.py
index b2f96b1c41..26b3bba0cd 100644
--- a/tests/unit/test_fm.py
+++ b/tests/unit/test_fm.py
@@ -330,3 +330,27 @@ def test_predictor_type(sagemaker_session):
     predictor = model.deploy(1, INSTANCE_TYPE)
 
     assert isinstance(predictor, FactorizationMachinesPredictor)
+
+
+def test_predictor_custom_serialization(sagemaker_session):
+    fm = FactorizationMachines(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
+    data = RecordSet(
+        "s3://{}/{}".format(BUCKET_NAME, PREFIX),
+        num_records=1,
+        feature_dim=FEATURE_DIM,
+        channel="train",
+    )
+    fm.fit(data, MINI_BATCH_SIZE)
+    model = fm.create_model()
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        INSTANCE_TYPE,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+
+    assert isinstance(predictor, FactorizationMachinesPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
diff --git a/tests/unit/test_ipinsights.py b/tests/unit/test_ipinsights.py
index 0673d375b4..a1a3959baf 100644
--- a/tests/unit/test_ipinsights.py
+++ b/tests/unit/test_ipinsights.py
@@ -305,3 +305,27 @@ def test_predictor_type(sagemaker_session):
     predictor = model.deploy(1, INSTANCE_TYPE)
 
     assert isinstance(predictor, IPInsightsPredictor)
+
+
+def test_predictor_custom_serialization(sagemaker_session):
+    ipinsights = IPInsights(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
+    data = RecordSet(
+        "s3://{}/{}".format(BUCKET_NAME, PREFIX),
+        num_records=1,
+        feature_dim=FEATURE_DIM,
+        channel="train",
+    )
+    ipinsights.fit(data, MINI_BATCH_SIZE)
+    model = ipinsights.create_model()
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        INSTANCE_TYPE,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+
+    assert isinstance(predictor, IPInsightsPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
diff --git a/tests/unit/test_kmeans.py b/tests/unit/test_kmeans.py
index ec5a8476a7..89c0580a95 100644
--- a/tests/unit/test_kmeans.py
+++ b/tests/unit/test_kmeans.py
@@ -272,3 +272,27 @@ def test_predictor_type(sagemaker_session):
     predictor = model.deploy(1, INSTANCE_TYPE)
 
     assert isinstance(predictor, KMeansPredictor)
+
+
+def test_predictor_custom_serialization(sagemaker_session):
+    kmeans = KMeans(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
+    data = RecordSet(
+        "s3://{}/{}".format(BUCKET_NAME, PREFIX),
+        num_records=1,
+        feature_dim=FEATURE_DIM,
+        channel="train",
+    )
+    kmeans.fit(data, MINI_BATCH_SIZE)
+    model = kmeans.create_model()
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        INSTANCE_TYPE,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+
+    assert isinstance(predictor, KMeansPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
diff --git a/tests/unit/test_knn.py b/tests/unit/test_knn.py
index 999de92f5c..e2546ba3b1 100644
--- a/tests/unit/test_knn.py
+++ b/tests/unit/test_knn.py
@@ -296,3 +296,27 @@ def test_predictor_type(sagemaker_session):
     predictor = model.deploy(1, INSTANCE_TYPE)
 
     assert isinstance(predictor, KNNPredictor)
+
+
+def test_predictor_custom_serialization(sagemaker_session):
+    knn = KNN(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
+    data = RecordSet(
+        "s3://{}/{}".format(BUCKET_NAME, PREFIX),
+        num_records=1,
+        feature_dim=FEATURE_DIM,
+        channel="train",
+    )
+    knn.fit(data, MINI_BATCH_SIZE)
+    model = knn.create_model()
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        INSTANCE_TYPE,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+
+    assert isinstance(predictor, KNNPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
diff --git a/tests/unit/test_lda.py b/tests/unit/test_lda.py
index 5e8579c595..0b4f3a8a65 100644
--- a/tests/unit/test_lda.py
+++ b/tests/unit/test_lda.py
@@ -232,3 +232,27 @@ def test_predictor_type(sagemaker_session):
     predictor = model.deploy(1, INSTANCE_TYPE)
 
     assert isinstance(predictor, LDAPredictor)
+
+
+def test_predictor_custom_serialization(sagemaker_session):
+    lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
+    data = RecordSet(
+        "s3://{}/{}".format(BUCKET_NAME, PREFIX),
+        num_records=1,
+        feature_dim=FEATURE_DIM,
+        channel="train",
+    )
+    lda.fit(data, MINI_BATCH_SZIE)
+    model = lda.create_model()
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        INSTANCE_TYPE,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+
+    assert isinstance(predictor, LDAPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
diff --git a/tests/unit/test_linear_learner.py b/tests/unit/test_linear_learner.py
index dc6e082827..6f8d2caf69 100644
--- a/tests/unit/test_linear_learner.py
+++ b/tests/unit/test_linear_learner.py
@@ -433,3 +433,27 @@ def test_predictor_type(sagemaker_session):
     predictor = model.deploy(1, INSTANCE_TYPE)
 
     assert isinstance(predictor, LinearLearnerPredictor)
+
+
+def test_predictor_custom_serialization(sagemaker_session):
+    lr = LinearLearner(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
+    data = RecordSet(
+        "s3://{}/{}".format(BUCKET_NAME, PREFIX),
+        num_records=1,
+        feature_dim=FEATURE_DIM,
+        channel="train",
+    )
+    lr.fit(data)
+    model = lr.create_model()
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        INSTANCE_TYPE,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+
+    assert isinstance(predictor, LinearLearnerPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
diff --git a/tests/unit/test_mxnet.py b/tests/unit/test_mxnet.py
index 091ce3497a..31fc38f265 100644
--- a/tests/unit/test_mxnet.py
+++ b/tests/unit/test_mxnet.py
@@ -581,6 +581,31 @@ def test_model_register_all_args(
     )
 
 
+@patch("sagemaker.utils.create_tar_file", MagicMock())
+def test_model_custom_serialization(
+    sagemaker_session, mxnet_inference_version, mxnet_inference_py_version, skip_if_mms_version
+):
+    model = MXNetModel(
+        MODEL_DATA,
+        role=ROLE,
+        entry_point=SCRIPT_PATH,
+        framework_version=mxnet_inference_version,
+        py_version=mxnet_inference_py_version,
+        sagemaker_session=sagemaker_session,
+    )
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        CPU,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+    assert isinstance(predictor, MXNetPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
+
+
 @patch("sagemaker.utils.repack_model")
 def test_model_mms_version(
     repack_model,
diff --git a/tests/unit/test_ntm.py b/tests/unit/test_ntm.py
index 3805d640a9..6aa07c469c 100644
--- a/tests/unit/test_ntm.py
+++ b/tests/unit/test_ntm.py
@@ -301,3 +301,27 @@ def test_predictor_type(sagemaker_session):
     predictor = model.deploy(1, INSTANCE_TYPE)
 
     assert isinstance(predictor, NTMPredictor)
+
+
+def test_predictor_custom_serialization(sagemaker_session):
+    ntm = NTM(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
+    data = RecordSet(
+        "s3://{}/{}".format(BUCKET_NAME, PREFIX),
+        num_records=1,
+        feature_dim=FEATURE_DIM,
+        channel="train",
+    )
+    ntm.fit(data, MINI_BATCH_SIZE)
+    model = ntm.create_model()
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        INSTANCE_TYPE,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+
+    assert isinstance(predictor, NTMPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
diff --git a/tests/unit/test_pca.py b/tests/unit/test_pca.py
index 4861b57c5a..79934b3a61 100644
--- a/tests/unit/test_pca.py
+++ b/tests/unit/test_pca.py
@@ -252,3 +252,27 @@ def test_predictor_type(sagemaker_session):
     predictor = model.deploy(1, INSTANCE_TYPE)
 
     assert isinstance(predictor, PCAPredictor)
+
+
+def test_predictor_custom_serialization(sagemaker_session):
+    pca = PCA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
+    data = RecordSet(
+        "s3://{}/{}".format(BUCKET_NAME, PREFIX),
+        num_records=1,
+        feature_dim=FEATURE_DIM,
+        channel="train",
+    )
+    pca.fit(data, MINI_BATCH_SIZE)
+    model = pca.create_model()
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        INSTANCE_TYPE,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+
+    assert isinstance(predictor, PCAPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
diff --git a/tests/unit/test_pytorch.py b/tests/unit/test_pytorch.py
index 1d33fb25dc..5fc4ddfca7 100644
--- a/tests/unit/test_pytorch.py
+++ b/tests/unit/test_pytorch.py
@@ -408,6 +408,34 @@ def test_model_image_accelerator(sagemaker_session):
     assert "Unsupported Python version: py2." in str(error)
 
 
+@patch("sagemaker.utils.create_tar_file", MagicMock())
+@patch("sagemaker.utils.repack_model", MagicMock())
+def test_model_custom_serialization(
+    sagemaker_session,
+    pytorch_inference_version,
+    pytorch_inference_py_version,
+):
+    model = PyTorchModel(
+        MODEL_DATA,
+        role=ROLE,
+        entry_point=SCRIPT_PATH,
+        framework_version=pytorch_inference_version,
+        py_version=pytorch_inference_py_version,
+        sagemaker_session=sagemaker_session,
+    )
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        GPU,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+    assert isinstance(predictor, PyTorchPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
+
+
 def test_model_prepare_container_def_no_instance_type_or_image():
     model = PyTorchModel(
         MODEL_DATA,
diff --git a/tests/unit/test_sklearn.py b/tests/unit/test_sklearn.py
index dc37d599b2..aa3fd491b8 100644
--- a/tests/unit/test_sklearn.py
+++ b/tests/unit/test_sklearn.py
@@ -20,8 +20,8 @@
 from mock import Mock
 from mock import patch
 
-from sagemaker.sklearn import SKLearn, SKLearnModel, SKLearnPredictor
 from sagemaker.fw_utils import UploadedCode
+from sagemaker.sklearn import SKLearn, SKLearnModel, SKLearnPredictor
 
 DATA_DIR = os.path.join(os.path.dirname(__file__), "..", "data")
 SCRIPT_PATH = os.path.join(DATA_DIR, "dummy_script.py")
@@ -408,6 +408,27 @@ def test_model(sagemaker_session, sklearn_version):
     assert isinstance(predictor, SKLearnPredictor)
 
 
+def test_model_custom_serialization(sagemaker_session, sklearn_version):
+    model = SKLearnModel(
+        "s3://some/data.tar.gz",
+        role=ROLE,
+        entry_point=SCRIPT_PATH,
+        framework_version=sklearn_version,
+        sagemaker_session=sagemaker_session,
+    )
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        CPU,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+    assert isinstance(predictor, SKLearnPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
+
+
 def test_attach(sagemaker_session, sklearn_version):
     training_image = "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:{}-cpu-{}".format(
         sklearn_version, PYTHON_VERSION
diff --git a/tests/unit/test_sparkml_serving.py b/tests/unit/test_sparkml_serving.py
index 4888639537..1ea9b7e3da 100644
--- a/tests/unit/test_sparkml_serving.py
+++ b/tests/unit/test_sparkml_serving.py
@@ -57,3 +57,12 @@ def test_predictor_type(sagemaker_session):
     predictor = sparkml.deploy(1, TRAIN_INSTANCE_TYPE)
 
     assert isinstance(predictor, SparkMLPredictor)
+
+
+def test_predictor_custom_serialization(sagemaker_session):
+    sparkml = SparkMLModel(sagemaker_session=sagemaker_session, model_data=MODEL_DATA, role=ROLE)
+    custom_serializer = Mock()
+    predictor = sparkml.deploy(1, TRAIN_INSTANCE_TYPE, serializer=custom_serializer)
+
+    assert isinstance(predictor, SparkMLPredictor)
+    assert predictor.serializer is custom_serializer
diff --git a/tests/unit/test_xgboost.py b/tests/unit/test_xgboost.py
index dd2b80c76e..0f155df987 100644
--- a/tests/unit/test_xgboost.py
+++ b/tests/unit/test_xgboost.py
@@ -429,6 +429,27 @@ def test_model(sagemaker_session, xgboost_framework_version):
     assert isinstance(predictor, XGBoostPredictor)
 
 
+def test_model_custom_serialization(sagemaker_session, xgboost_framework_version):
+    model = XGBoostModel(
+        "s3://some/data.tar.gz",
+        role=ROLE,
+        framework_version=xgboost_framework_version,
+        entry_point=SCRIPT_PATH,
+        sagemaker_session=sagemaker_session,
+    )
+    custom_serializer = Mock()
+    custom_deserializer = Mock()
+    predictor = model.deploy(
+        1,
+        CPU,
+        serializer=custom_serializer,
+        deserializer=custom_deserializer,
+    )
+    assert isinstance(predictor, XGBoostPredictor)
+    assert predictor.serializer is custom_serializer
+    assert predictor.deserializer is custom_deserializer
+
+
 def test_training_image_uri(sagemaker_session, xgboost_framework_version):
     xgboost = XGBoost(
         entry_point=SCRIPT_PATH,