Adding a C# example for Cloud ML Engine's online prediction API

ml_engine/online_prediction/README.md

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+https://cloud.google.com/ml-engine/docs/concepts/prediction-overview

ml_engine/online_prediction/predict.cs

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+# Copyright 2017 Google Inc. All Rights Reserved.
+#
+# 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.
+
+using System;
+using System.Collections.Generic;
+using System.Net.Http;
+using System.Net.Http.Headers;
+using System.Text;
+using System.Threading.Tasks;
+using Google.Apis.Auth.OAuth2;
+using Newtonsoft.Json;
+
+namespace prediction_client
+{
+    class Person
+    {
+        public int age { get; set; }
+        public string workclass { get; set; }
+        public string education { get; set; }
+        public int education_num { get; set; }
+        public string marital_status { get; set; }
+        public string occupation { get; set; }
+        public string relationship { get; set; }
+        public string race { get; set; }
+        public string gender { get; set; }
+        public int capital_gain { get; set; }
+        public int capital_loss { get; set; }
+        public int hours_per_week { get; set; }
+        public string native_country { get; set; }
+    }
+
+    class MainClass
+    {
+        public static void Main(string[] args)
+        {
+            RunAsync().Wait();
+        }
+
+	static string project = "YOUR_PROJECT";
+	static string model = "census";  // Name of deployed model
+
+        static HttpClient client = new HttpClient();
+
+        static async Task RunAsync()
+        {
+            client.BaseAddress = new Uri("https://ml.googleapis.com/v1/");
+            client.DefaultRequestHeaders.Accept.Clear();
+            client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
+
+            try
+            {
+                Person person = new Person
+                {
+                    age = 25,
+                    workclass = " Private",
+                    education = " 11th",
+                    education_num = 7,
+                    marital_status = " Never - married",
+                    occupation = " Machine - op - inspct",
+                    relationship = " Own - child",
+                    race = " Black",
+                    gender = " Male",
+                    capital_gain = 0,
+                    capital_loss = 0,
+                    hours_per_week = 40,
+                    native_country = " United - Stats"
+                };
+                var instances = new List<Person> { person };
+
+                var predictions = await Predict(project, model, instances);
+                Console.WriteLine(predictions);
+            }
+            catch (Exception e)
+            {
+                Console.WriteLine(e.Message);
+            }
+
+            Console.ReadLine();
+        }
+
+        // [START predict_json]
+        static async Task<dynamic> Predict(String project, String model, List<Person> instances, String version = null)
+        {
+            GoogleCredential credential = await GoogleCredential.GetApplicationDefaultAsync();
+            var bearer_token = await credential.UnderlyingCredential.GetAccessTokenForRequestAsync();
+            client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", bearer_token);
+
+            var version_suffix = version == null ? "" : $"/version/{version}";
+            var model_uri = $"projects/{project}/models/{model}{version_suffix}";
+			var predict_uri = $"{model_uri}:predict";
+
+            var request = new { instances = instances };
+            var content = new StringContent(JsonConvert.SerializeObject(request), Encoding.UTF8, "application/json");
+
+            var responseMessage = await client.PostAsync(predict_uri, content);
+            responseMessage.EnsureSuccessStatusCode();
+
+            var responseBody = await responseMessage.Content.ReadAsStringAsync();
+            dynamic response = JsonConvert.DeserializeObject(responseBody);
+
+            return response.predictions;
+        }
+        // [END predict_json]
+    }
+}
+