Kubeflow and Beyond: Automation of Model Training, Deployment and Testing

Very often a workflow of training models and delivering them to the production environment contains loads of manual work. This could entail either building a Docker image and deploying it to the Kubernetes cluster, or packing the model to the Python package and installing it to your Python application. Or even changing your Java classes with the defined weights and re-compiling the whole project. Not to mention that all of this should be followed by testing your model’s performance. It hardly could be named “continuous delivery” if you do it all manually. Imagine you could run the whole process of assembling/training/deploying/testing/running model via a single command in your terminal. In this webinar, we present a way to build the whole workflow of data gathering/model training/model deployment/model testing into a single flow and run it with a single command.