Summary
To summarize, Kubeflow provides an easy-to-deploy, easy-to-use toolchain that will allow data scientists to integrate the various resources they will need to run models on Kubernetes, such as Jupyter Notebooks, Kubernetes deployment files, and ML libraries such as PyTorch and TensorFlow.
Another popular ML task that Kubeflow considerably simplifies is working with Jupyter Notebooks. You can build notebooks and share them with your team or teams using Kubeflow’s built-in notebook services, which you can access via the UI. In this chapter, we learned how to set up an ML pipeline that will develop and deploy an example model using the Kubeflow ML platform. We also recognized that Kubeflow on MicroK8s is easy to set up and configure, as well as lightweight and capable of simulating real-world conditions while constructing, migrating, and deploying pipelines.
In the next chapter, you will learn how to deploy and run serverless applications using the Knative and OpenFaaS...