<p>I am looking to host 5 deep learning models where data preprocessing/postprocessing is required.</p>

<p>It seems straightforward to host each model using TF serving (and Kubernetes to manage the containers), but if that is the case, where should the data pre and post-processing take place?</p>

<p>Additional options to josephkibe's answer, you can:</p>

<ol>
<li><p>Implementing processing into model itself (see signatures for keras models and input receivers for estimators in <a href="https://www.tensorflow.org/guide/saved_model" rel="nofollow noreferrer">SavedModel guide</a>). </p></li>
<li><p>Install <a href="https://docs.seldon.io/projects/seldon-core/en/latest/" rel="nofollow noreferrer">Seldon-core</a>. It is a whole framework for serving that handles building images and networking. It builds service as a graph of pods with different API's, one of them are transformers that pre/post-process data.  </p></li>
</ol>
