title: Kubeflow vs other options
body: <p>I am trying to find when it makes sense to create your own Kubeflow MLOps platform:</p>
  
  <ul>
  <li>If you are Tensorflow only shop, do you still need Kubeflow? Why not TFX only? Orchestration can be done with Airflow.</li>
  <li>Why use Kubeflow if all you are using scikit-learn as it does not support GPU, distributed training anyways? Orchestration can be done with Airflow.</li>
  <li>If you are convinced to use Kubeflow, cloud providers (Azure and GCP) are delivering ML pipeline concept (Google is using Kubeflow under the hood) as managed services. When it makes sense to deploy your own Kubeflow environment then? Even if you have a requirement to deploy on-prem, you have the option to use the cloud resources (nodes and data on cloud) to train your models, and only deploy the model to on-prem. Thus, using Azure or GCP AI Platform as managed service makes the most sense to deliver ML pipelines?</li>
  </ul>