@@ -241,7 +241,7 @@ def _are_futures_done(self) -> bool:
 return self.__latest_future is None
 
 def wait(self):
-"""Helper method to that blocks until all futures are complete."""
+"""Helper method that blocks until all futures are complete."""
 future = self.__latest_future
 if future:
 futures.wait([future], return_when=futures.FIRST_EXCEPTION)
@@ -974,7 +974,11 @@ def _sync_object_with_future_result(
 "_gca_resource",
 "credentials",
 ]
-optional_sync_attributes = ["_prediction_client"]
+optional_sync_attributes = [
+"_prediction_client",
+"_authorized_session",
+"_raw_predict_request_url",
+]
 
 for attribute in sync_attributes:
 setattr(self, attribute, getattr(result, attribute))

@@ -92,3 +92,6 @@
 # that is being used for usage metrics tracking purposes.
 # For more details on go/oneplatform-api-analytics
 USER_AGENT_SDK_COMMAND = ""
+
+# Needed for Endpoint.raw_predict
+DEFAULT_AUTHED_SCOPES = ["https://www.googleapis.com/auth/cloud-platform"]

@@ -20,6 +20,7 @@
 import re
 import shutil
 import tempfile
+import requests
 from typing import (
 Any,
 Dict,
@@ -35,9 +36,11 @@
 from google.api_core import operation
 from google.api_core import exceptions as api_exceptions
 from google.auth import credentials as auth_credentials
+from google.auth.transport import requests as google_auth_requests
 
 from google.cloud import aiplatform
 from google.cloud.aiplatform import base
+from google.cloud.aiplatform import constants
 from google.cloud.aiplatform import explain
 from google.cloud.aiplatform import initializer
 from google.cloud.aiplatform import jobs
@@ -69,6 +72,8 @@
 _DEFAULT_MACHINE_TYPE = "n1-standard-2"
 _DEPLOYING_MODEL_TRAFFIC_SPLIT_KEY = "0"
 _SUCCESSFUL_HTTP_RESPONSE = 300
+_RAW_PREDICT_DEPLOYED_MODEL_ID_KEY = "X-Vertex-AI-Deployed-Model-Id"
+_RAW_PREDICT_MODEL_RESOURCE_KEY = "X-Vertex-AI-Model"
 
 _LOGGER = base.Logger(__name__)
 
@@ -200,6 +205,8 @@ def __init__(
 location=self.location,
 credentials=credentials,
 )
+self.authorized_session = None
+self.raw_predict_request_url = None
 
 def _skipped_getter_call(self) -> bool:
 """Check if GAPIC resource was populated by call to get/list API methods
@@ -1389,16 +1396,15 @@ def update(
 """Updates an endpoint.
 
 Example usage:
-
-my_endpoint = my_endpoint.update(
-display_name='my-updated-endpoint',
-description='my updated description',
-labels={'key': 'value'},
-traffic_split={
-'123456': 20,
-'234567': 80,
-},
-)
+my_endpoint = my_endpoint.update(
+display_name='my-updated-endpoint',
+description='my updated description',
+labels={'key': 'value'},
+traffic_split={
+'123456': 20,
+'234567': 80,
+},
+)
 
 Args:
 display_name (str):
@@ -1481,6 +1487,7 @@ def predict(
 instances: List,
 parameters: Optional[Dict] = None,
 timeout: Optional[float] = None,
+use_raw_predict: Optional[bool] = False,
 ) -> Prediction:
 """Make a prediction against this Endpoint.
 
@@ -1505,29 +1512,80 @@ def predict(
 [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata]
 ``parameters_schema_uri``.
 timeout (float): Optional. The timeout for this request in seconds.
+use_raw_predict (bool):
+Optional. Default value is False. If set to True, the underlying prediction call will be made
+against Endpoint.raw_predict(). Note that model version information will
+not be available in the prediciton response using raw_predict.
 
 Returns:
 prediction (aiplatform.Prediction):
 Prediction with returned predictions and Model ID.
 """
 self.wait()
+if use_raw_predict:
+raw_predict_response = self.raw_predict(
+body=json.dumps({"instances": instances, "parameters": parameters}),
+headers={"Content-Type": "application/json"},
+)
+json_response = json.loads(raw_predict_response.text)
+return Prediction(
+predictions=json_response["predictions"],
+deployed_model_id=raw_predict_response.headers[
+_RAW_PREDICT_DEPLOYED_MODEL_ID_KEY
+],
+model_resource_name=raw_predict_response.headers[
+_RAW_PREDICT_MODEL_RESOURCE_KEY
+],
+)
+else:
+prediction_response = self._prediction_client.predict(
+endpoint=self._gca_resource.name,
+instances=instances,
+parameters=parameters,
+timeout=timeout,
+)
 
-prediction_response = self._prediction_client.predict(
-endpoint=self._gca_resource.name,
-instances=instances,
-parameters=parameters,
-timeout=timeout,
-)
+return Prediction(
+predictions=[
+json_format.MessageToDict(item)
+for item in prediction_response.predictions.pb
+],
+deployed_model_id=prediction_response.deployed_model_id,
+model_version_id=prediction_response.model_version_id,
+model_resource_name=prediction_response.model,
+)
 
-return Prediction(
-predictions=[
-json_format.MessageToDict(item)
-for item in prediction_response.predictions.pb
-],
-deployed_model_id=prediction_response.deployed_model_id,
-model_version_id=prediction_response.model_version_id,
-model_resource_name=prediction_response.model,
-)
+def raw_predict(
+self, body: bytes, headers: Dict[str, str]
+) -> requests.models.Response:
+"""Makes a prediction request using arbitrary headers.
+
+Example usage:
+my_endpoint = aiplatform.Endpoint(ENDPOINT_ID)
+response = my_endpoint.raw_predict(
+body = b'{"instances":[{"feat_1":val_1, "feat_2":val_2}]}'
+headers = {'Content-Type':'application/json'}
+)
+status_code = response.status_code
+results = json.dumps(response.text)
+
+Args:
+body (bytes):
+The body of the prediction request in bytes. This must not exceed 1.5 mb per request.
+headers (Dict[str, str]):
+The header of the request as a dictionary. There are no restrictions on the header.
+
+Returns:
+A requests.models.Response object containing the status code and prediction results.
+"""
+if not self.authorized_session:
+self.credentials._scopes = constants.base.DEFAULT_AUTHED_SCOPES
+self.authorized_session = google_auth_requests.AuthorizedSession(
+self.credentials
+)
+self.raw_predict_request_url = f"https://{self.location}-{constants.base.API_BASE_PATH}/v1/projects/{self.project}/locations/{self.location}/endpoints/{self.name}:rawPredict"
+
+return self.authorized_session.post(self.raw_predict_request_url, body, headers)
 
 def explain(
 self,
@@ -2004,7 +2062,7 @@ def _http_request(
 def predict(self, instances: List, parameters: Optional[Dict] = None) -> Prediction:
 """Make a prediction against this PrivateEndpoint using a HTTP request.
 This method must be called within the network the PrivateEndpoint is peered to.
-The predict() call will fail otherwise. To check, use `PrivateEndpoint.network`.
+Otherwise, the predict() call will fail with error code 404. To check, use `PrivateEndpoint.network`.
 
 Example usage:
 response = my_private_endpoint.predict(instances=[...])
@@ -2062,6 +2120,39 @@ def predict(self, instances: List, parameters: Optional[Dict] = None) -> Predict
 deployed_model_id=self._gca_resource.deployed_models[0].id,
 )
 
+def raw_predict(
+self, body: bytes, headers: Dict[str, str]
+) -> requests.models.Response:
+"""Make a prediction request using arbitrary headers.
+This method must be called within the network the PrivateEndpoint is peered to.
+Otherwise, the predict() call will fail with error code 404. To check, use `PrivateEndpoint.network`.
+
+Example usage:
+my_endpoint = aiplatform.PrivateEndpoint(ENDPOINT_ID)
+response = my_endpoint.raw_predict(
+body = b'{"instances":[{"feat_1":val_1, "feat_2":val_2}]}'
+headers = {'Content-Type':'application/json'}
+)
+status_code = response.status_code
+results = json.dumps(response.text)
+
+Args:
+body (bytes):
+The body of the prediction request in bytes. This must not exceed 1.5 mb per request.
+headers (Dict[str, str]):
+The header of the request as a dictionary. There are no restrictions on the header.
+
+Returns:
+A requests.models.Response object containing the status code and prediction results.
+"""
+self.wait()
+return self._http_request(
+method="POST",
+url=self.predict_http_uri,
+body=body,
+headers=headers,
+)
+
 def explain(self):
 raise NotImplementedError(
 f"{self.__class__.__name__} class does not support 'explain' as of now."

@@ -80,9 +80,9 @@
 "uvicorn >= 0.16.0",
 ]
 
-private_endpoints_extra_require = [
-"urllib3 >=1.21.1, <1.27",
-]
+endpoint_extra_require = ["requests >= 2.28.1"]
+
+private_endpoints_extra_require = ["urllib3 >=1.21.1, <1.27", "requests >= 2.28.1"]
 full_extra_require = list(
 set(
 tensorboard_extra_require
@@ -92,6 +92,7 @@
 + featurestore_extra_require
 + pipelines_extra_require
 + datasets_extra_require
++ endpoint_extra_require
 + vizier_extra_require
 + prediction_extra_require
 + private_endpoints_extra_require
@@ -136,6 +137,7 @@
 "google-cloud-resource-manager >= 1.3.3, < 3.0.0dev",
 ),
 extras_require={
+"endpoint": endpoint_extra_require,
 "full": full_extra_require,
 "metadata": metadata_extra_require,
 "tensorboard": tensorboard_extra_require,

@@ -0,0 +1,61 @@
+# -*- coding: utf-8 -*-
+
+# Copyright 2022 Google LLC
+#
+# Licensed 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 json
+
+from google.cloud import aiplatform
+
+from tests.system.aiplatform import e2e_base
+
+_PERMANENT_IRIS_ENDPOINT_ID = "4966625964059525120"
+_PREDICTION_INSTANCE = {
+"petal_length": "3.0",
+"petal_width": "3.0",
+"sepal_length": "3.0",
+"sepal_width": "3.0",
+}
+
+
+class TestModelInteractions(e2e_base.TestEndToEnd):
+_temp_prefix = ""
+endpoint = aiplatform.Endpoint(_PERMANENT_IRIS_ENDPOINT_ID)
+
+def test_prediction(self):
+# test basic predict
+prediction_response = self.endpoint.predict(instances=[_PREDICTION_INSTANCE])
+assert len(prediction_response.predictions) == 1
+
+# test predict(use_raw_predict = True)
+prediction_with_raw_predict = self.endpoint.predict(
+instances=[_PREDICTION_INSTANCE], use_raw_predict=True
+)
+assert (
+prediction_with_raw_predict.deployed_model_id
+== prediction_response.deployed_model_id
+)
+assert (
+prediction_with_raw_predict.model_resource_name
+== prediction_response.model_resource_name
+)
+
+# test raw_predict
+raw_prediction_response = self.endpoint.raw_predict(
+json.dumps({"instances": [_PREDICTION_INSTANCE]}),
+{"Content-Type": "application/json"},
+)
+assert raw_prediction_response.status_code == 200
+assert len(json.loads(raw_prediction_response.text)) == 1

@@ -29,7 +29,7 @@
 
 
 @pytest.mark.usefixtures("delete_staging_bucket", "tear_down_resources")
-class TestModel(e2e_base.TestEndToEnd):
+class TestModelUploadAndUpdate(e2e_base.TestEndToEnd):
 
 _temp_prefix = "temp_vertex_sdk_e2e_model_upload_test"
 
@@ -65,9 +65,8 @@ def test_upload_and_deploy_xgboost_model(self, shared_state):
 # See https://.com/googleapis/python-aiplatform/issues/773
 endpoint = model.deploy(machine_type="n1-standard-2")
 shared_state["resources"].append(endpoint)
-predict_response = endpoint.predict(instances=[[0, 0, 0]])
-assert len(predict_response.predictions) == 1
 
+# test model update
 model = model.update(
 display_name="new_name",
 description="new_description",