diff --git a/README.md b/README.md
index dc9af111..a3373b70 100644
--- a/README.md
+++ b/README.md
@@ -27,6 +27,7 @@ model.
     *   [Evaluation and Inference](#evaluation-and-inference)
 *   [Create Your Own Dataset Files](#create-your-own-dataset-files)
 *   [Training without this Starter Code](#training-without-this-starter-code)
+*   [Export Your Model for MediaPipe Inference](#export-your-model-for-mediapipe-inference)
 *   [More Documents](#more-documents)
 *   [About This Project](#about-this-project)
 
@@ -321,6 +322,16 @@ and the following for the inference code:
 num examples processed: 8192 elapsed seconds: 14.85
 ```
 
+## Export Your Model for MediaPipe Inference
+To run inference with your model in [MediaPipe inference
+demo](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/youtube8m#steps-to-run-the-youtube-8m-inference-graph-with-the-yt8m-dataset), you need to export your checkpoint to a SavedModel.
+
+Example command:
+```sh
+python export_model_mediapipe.py --checkpoint_file  ~/yt8m/models/frame/sample_model/inference_model/segment_inference_model --output_dir /tmp/mediapipe/saved_model/
+```
+
+
 ## Create Your Own Dataset Files
 
 You can create your dataset files from your own videos. Our
diff --git a/export_model_mediapipe.py b/export_model_mediapipe.py
new file mode 100644
index 00000000..d81cd9c6
--- /dev/null
+++ b/export_model_mediapipe.py
@@ -0,0 +1,60 @@
+# Lint as: python3
+import numpy as np
+import tensorflow as tf
+from tensorflow import app
+from tensorflow import flags
+
+FLAGS = flags.FLAGS
+
+
+def main(unused_argv):
+  # Get the input tensor names to be replaced.
+  tf.reset_default_graph()
+  meta_graph_location = FLAGS.checkpoint_file + ".meta"
+  tf.train.import_meta_graph(meta_graph_location, clear_devices=True)
+
+  input_tensor_name = tf.get_collection("input_batch_raw")[0].name
+  num_frames_tensor_name = tf.get_collection("num_frames")[0].name
+
+  # Create output graph.
+  saver = tf.train.Saver()
+  tf.reset_default_graph()
+
+  input_feature_placeholder = tf.placeholder(
+        tf.float32, shape=(None, None, 1152))
+  num_frames_placeholder = tf.placeholder(tf.int32, shape=(None, 1))
+
+  saver = tf.train.import_meta_graph(
+      meta_graph_location,
+      input_map={
+          input_tensor_name: input_feature_placeholder,
+          num_frames_tensor_name: tf.squeeze(num_frames_placeholder, axis=1)
+      },
+      clear_devices=True)
+  predictions_tensor = tf.get_collection("predictions")[0]
+
+  with tf.Session() as sess:
+    print("restoring variables from " + FLAGS.checkpoint_file)
+    saver.restore(sess, FLAGS.checkpoint_file)
+    tf.saved_model.simple_save(
+        sess,
+        FLAGS.output_dir,
+        inputs={'rgb_and_audio': input_feature_placeholder,
+                'num_frames': num_frames_placeholder},
+        outputs={'predictions': predictions_tensor})
+
+    # Try running inference.
+    predictions = sess.run(
+       [predictions_tensor],
+       feed_dict={
+          input_feature_placeholder: np.zeros((3, 7, 1152), dtype=np.float32),
+          num_frames_placeholder: np.array([[7]], dtype=np.int32)})
+    print('Test inference:', predictions)
+
+    print('Model saved to ', FLAGS.output_dir)
+
+
+if __name__ == '__main__':
+  flags.DEFINE_string('checkpoint_file', None, 'Path to the checkpoint file.')
+  flags.DEFINE_string('output_dir', None, 'SavedModel output directory.')
+  app.run(main)