Tensorflow Serving: When to use it rather than simple inference inside Flask service? : <p>I am serving a model trained using object detection API. Here is how I did it:</p>

<ul>
<li><p>Create a Tensorflow service on port 9000 as described in the <a href="https://www.tensorflow.org/serving/serving_basic" rel="noreferrer">basic tutorial</a></p></li>
<li><p>Create a python code calling this service using predict_pb2 from tensorflow_serving.apis similar to <a href="https://medium.freecodecamp.org/how-to-deploy-an-object-detection-model-with-tensorflow-serving-d6436e65d1d9" rel="noreferrer">this</a></p></li>
<li>Call this code inside a Flask server to make the service available with HTTP</li>
</ul>

<p>Still, I could have done things much easier the following way :</p>

<ul>
<li>Create a python code for inference like in the <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb" rel="noreferrer">example in object detection repo</a></li>
<li>Call this code inside a Flask server to make the service available with HTTP</li>
</ul>

<p>As you can see, I could have skipped the use of Tensorflow serving.</p>

<p>So, is there any good reason to use Tensorflow serving in my case ? If not, what are the cases where I should use it ?</p>
