<p>I am a relatively seasoned AI practitioner however, I am a complete newbie when it comes to deployment of these models. I followed an online tutorial that deployed the model locally using Docker Desktop. It created a stack of containers for frontend and backend. I installed Tensorflow in each of these containers to run the AI model (RUN pip3 install tensorflow in the Dockerfile). However, I can't deploy it on Kubernetes.
I hecked the option which allowed Docker Stacks to be sent to Kubernetes. I can see both frontend and backend images when I run <code>docker images</code>. The next step that I did was that I created a GCP project and created cluster in it. Then I pushed these images after tagging them in the specific format <code>gcr.io/project/name:tag</code> to both front end and back end. Then I deployed both and then exposed them as well as fdep (frontend) and bdep (backend). Both of them are running correctly as seen here:
[![enter image description here][1]][1]</p>

<p>However, when I go to front end external ip and run the model, nothing happens. As if the back end is not outputting anything.
Here is what I get when I use postman to post a request on backend external IP:
[![enter image description here][2]][2]</p>

<p>Any help here. What am I doing wrong?</p>

Muhammad Aleem
<p>Since this multi-container docker app that was not originally developed for kubernetes, make sure specify a name when generating the service for your backend, </p>

<p>kubectl expose deployment bdep --port 8081 --name (name-that-the-front-end-apps-expect)</p>

<p>In your case, without the option --name, the service name defaults to the deployment name "bdep", but the front end apps are expecting the name "backend".</p>
