diff --git a/csharp/src/Microsoft.ML.OnnxRuntime/Training/TrainingSession.shared.cs b/csharp/src/Microsoft.ML.OnnxRuntime/Training/TrainingSession.shared.cs
index 877677dcad..fec0d46e96 100644
--- a/csharp/src/Microsoft.ML.OnnxRuntime/Training/TrainingSession.shared.cs
+++ b/csharp/src/Microsoft.ML.OnnxRuntime/Training/TrainingSession.shared.cs
@@ -282,6 +282,48 @@ namespace Microsoft.ML.OnnxRuntime
             }
         }
 
+        /// <summary>
+        /// This function performs a training step that computes the outputs of the training model and the gradients
+        /// of the trainable parameters for the given OrtValue inputs. The train step is performed based on the training model
+        /// that was provided to the training session.
+        /// The TrainStep method is equivalent of running forward propagation and backward propagation in a single
+        /// step.
+        /// The gradients computed are stored inside the training session state so they can be later consumed
+        /// by the OptimizerStep function.
+        /// The gradients can be lazily reset by invoking the LazyResetGrad function.
+        /// Example usage:
+        /// <code>
+        /// using OrtValue x = OrtValue.CreateTensorValueFromMemory(...);
+        /// using OrtValue label = OrtValue.CreateTensorValueFromMemory(...);
+        /// List<OrtValue> inputValues = new List<OrtValue> { x, label };
+        /// using (var loss = trainingSession.TrainStep(inputValues))
+        /// {
+        ///     // process output values
+        /// }
+        /// </code>
+        /// </summary>
+        /// <param name="inputValues">Specify a collection of <see cref="OrtValue"/> that indicates the input values to the training model.</param>
+        /// <returns>Output Tensors in a Collection of NamedOnnxValue. User must dispose the output.</returns>
+        public IDisposableReadOnlyCollection<OrtValue> TrainStep(IReadOnlyCollection<OrtValue> inputValues)
+        {
+            IntPtr[] inputValuesArray = GetOrtValuesHandles(inputValues);
+            IntPtr[] outputValuesArray = new IntPtr[(int)_trainOutputCount];
+
+            NativeApiStatus.VerifySuccess(NativeTrainingMethods.OrtTrainStep(_nativeHandle, IntPtr.Zero, (UIntPtr)inputValues.Count,
+                inputValuesArray, (UIntPtr)_trainOutputCount, outputValuesArray));
+
+
+            var disposableHandles = new DisposableOrtValueHandleArray(outputValuesArray);
+            try
+            {
+                return CreateDisposableResult(disposableHandles);
+            }
+            finally
+            {
+                disposableHandles.Dispose();
+            }
+        }
+
         /// <summary>
         /// Convert native OrtValue handles to OrtValue instances
         /// in an exceptions safe manner.
@@ -370,6 +412,42 @@ namespace Microsoft.ML.OnnxRuntime
                 inputValuesArray, (UIntPtr)outputValues.Count, outputValuesArray));
         }
 
+        /// <summary>
+        /// This function performs an eval step that computes the outputs of the eval model for the given inputs.
+        /// Inputs are expected to be of type OrtValue. The eval step is performed based on the eval model that was
+        /// provided to the training session.
+        /// Example usage:
+        /// <code>
+        /// using OrtValue x = OrtValue.CreateTensorValueFromMemory(...);
+        /// using OrtValue label = OrtValue.CreateTensorValueFromMemory(...);
+        /// List<OrtValue> inputValues = new List<OrtValue> { x, label };
+        /// using (var loss = trainingSession.EvalSteps(inputValues))
+        /// {
+        ///     // process output values
+        /// }
+        /// </code>
+        /// </summary>
+        /// <param name="inputValues">Specify a collection of <see cref="OrtValue"/> that indicates the input values to the eval model.</param>
+        public IDisposableReadOnlyCollection<OrtValue> EvalStep(IReadOnlyCollection<OrtValue> inputValues)
+        {
+            IntPtr[] inputValuesArray = GetOrtValuesHandles(inputValues);
+            IntPtr[] outputValuesArray = new IntPtr[(int)_evalOutputCount];
+
+            NativeApiStatus.VerifySuccess(NativeTrainingMethods.OrtEvalStep(_nativeHandle, IntPtr.Zero, (UIntPtr)inputValues.Count,
+                inputValuesArray, (UIntPtr)_evalOutputCount, outputValuesArray));
+
+
+            var disposableHandles = new DisposableOrtValueHandleArray(outputValuesArray);
+            try
+            {
+                return CreateDisposableResult(disposableHandles);
+            }
+            finally
+            {
+                disposableHandles.Dispose();
+            }
+        }
+
 
         /// <summary>
         /// Sets the learning rate for this training session.
@@ -702,6 +780,35 @@ namespace Microsoft.ML.OnnxRuntime
             return valuesArray;
         }
 
+        private IntPtr[] GetOrtValuesHandles(IReadOnlyCollection<OrtValue> inputValues)
+        {
+            var valuesArray = new IntPtr[inputValues.Count];
+            for (int index = 0; index < inputValues.Count; ++index)
+            {
+                valuesArray[index] = inputValues.ElementAt(index).Handle;
+            }
+            return valuesArray;
+        }
+
+        private static IDisposableReadOnlyCollection<OrtValue> CreateDisposableResult(DisposableOrtValueHandleArray disposableHandles)
+        {
+            var outputValues = new DisposableList<OrtValue>(disposableHandles.Span.Length);
+            try
+            {
+                for (int i = 0; i < disposableHandles.Span.Length; i++)
+                {
+                    outputValues.Add(new OrtValue(disposableHandles.Span[i]));
+                    disposableHandles.Span[i] = IntPtr.Zero;
+                }
+                return outputValues;
+            }
+            catch (Exception)
+            {
+                outputValues.Dispose();
+                throw;
+            }
+        }
+
         private IntPtr[] ConvertNamesToUtf8(IReadOnlyCollection<string> names, DisposableList<IDisposable> cleanupList)
         {
             cleanupList.Capacity += names.Count;
diff --git a/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/TrainingTest.cs b/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/TrainingTest.cs
index 68b1d5bcc6..9b72326201 100644
--- a/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/TrainingTest.cs
+++ b/csharp/test/Microsoft.ML.OnnxRuntime.Tests.Common/TrainingTest.cs
@@ -612,6 +612,81 @@ namespace Microsoft.ML.OnnxRuntime.Tests
             }
         }
 
+        [Fact(DisplayName = "TestTrainingSessionTrainStepWithOrtValues")]
+        public void TestTrainingSessionTrainStepWithOrtValues()
+        {
+            string checkpointPath = Path.Combine(Directory.GetCurrentDirectory(), "checkpoint.ckpt");
+            using (var cleanUp = new DisposableListTest<IDisposable>())
+            {
+                var state = CheckpointState.LoadCheckpoint(checkpointPath);
+                cleanUp.Add(state);
+                Assert.NotNull(state);
+                string trainingPath = Path.Combine(Directory.GetCurrentDirectory(), "training_model.onnx");
+                string optimizerPath = Path.Combine(Directory.GetCurrentDirectory(), "adamw.onnx");
+
+                var trainingSession = new TrainingSession(state, trainingPath, optimizerPath);
+                cleanUp.Add(trainingSession);
+
+                float[] expectedOutput = TestDataLoader.LoadTensorFromFile("loss_1.out");
+                var expectedOutputDimensions = new int[] { 1 };
+                float[] inputData = TestDataLoader.LoadTensorFromFile("input-0.in");
+                long[] inputShape = { 2, 784 };
+                Int32[] labelsData = { 1, 1 };
+                long[] labelsShape = { 2 };
+
+                using OrtValue inputOrtValue = OrtValue.CreateTensorValueFromMemory<float>(inputData, inputShape);
+                using OrtValue labelsOrtValue = OrtValue.CreateTensorValueFromMemory<Int32>(labelsData, labelsShape);
+                var inputValues = new List<OrtValue> { inputOrtValue, labelsOrtValue };
+
+                using (var results = trainingSession.TrainStep(inputValues))
+                {
+                    Assert.Single(results);
+                    var outputOrtValue = results[0];
+                    Assert.True(outputOrtValue.IsTensor);
+                    var resultSpan = outputOrtValue.GetTensorDataAsSpan<float>().ToArray();
+                    Assert.Equal(expectedOutput, resultSpan, new FloatComparer());
+                }
+            }
+        }
+
+        [Fact(DisplayName = "TestTrainingSessionEvalStepWithOrtValues")]
+        public void TestTrainingSessionEvalStepWithOrtValues()
+        {
+            string checkpointPath = Path.Combine(Directory.GetCurrentDirectory(), "checkpoint.ckpt");
+            using (var cleanUp = new DisposableListTest<IDisposable>())
+            {
+                var state = CheckpointState.LoadCheckpoint(checkpointPath);
+                cleanUp.Add(state);
+                Assert.NotNull(state);
+                string trainingPath = Path.Combine(Directory.GetCurrentDirectory(), "training_model.onnx");
+                string optimizerPath = Path.Combine(Directory.GetCurrentDirectory(), "adamw.onnx");
+                string evalPath = Path.Combine(Directory.GetCurrentDirectory(), "eval_model.onnx");
+
+                var trainingSession = new TrainingSession(state, trainingPath, evalPath, optimizerPath);
+                cleanUp.Add(trainingSession);
+
+                float[] expectedOutput = TestDataLoader.LoadTensorFromFile("loss_1.out");
+                var expectedOutputDimensions = new int[] { 1 };
+                float[] inputData = TestDataLoader.LoadTensorFromFile("input-0.in");
+                long[] inputShape = { 2, 784 };
+                Int32[] labelsData = { 1, 1 };
+                long[] labelsShape = { 2 };
+
+                using OrtValue inputOrtValue = OrtValue.CreateTensorValueFromMemory<float>(inputData, inputShape);
+                using OrtValue labelsOrtValue = OrtValue.CreateTensorValueFromMemory<Int32>(labelsData, labelsShape);
+                var inputValues = new List<OrtValue> { inputOrtValue, labelsOrtValue };
+
+                using (var results = trainingSession.EvalStep(inputValues))
+                {
+                    Assert.Single(results);
+                    var outputOrtValue = results[0];
+                    Assert.True(outputOrtValue.IsTensor);
+                    var resultSpan = outputOrtValue.GetTensorDataAsSpan<float>().ToArray();
+                    Assert.Equal(expectedOutput, resultSpan, new FloatComparer());
+                }
+            }
+        }
+
         internal class FloatComparer : IEqualityComparer<float>
         {
             private float atol = 1e-3f;