Going Serverless with Knative and OpenFaaS Frameworks
In the last chapter, we discussed Kubeflow, which provides an easy-to-deploy, simple-to-use toolchain for data scientists to integrate the various resources they will need to run models on Kubernetes, such as Jupyter notebooks, Kubernetes deployment files, and machine learning libraries such as PyTorch and TensorFlow.
By using Kubeflow’s built-in Notebooks services, you can create notebooks and share them with your teams. We also went over how to set up a machine learning pipeline to develop and deploy an example model using the Kubeflow machine learning platform. Additionally, we established that Kubeflow on MicroK8s is simple to set up and configure, lightweight, and capable of simulating real-world conditions while building, migrating, and deploying pipelines.
In this chapter, we will look at the most popular open source serverless frameworks that extend Kubernetes with components for deploying, operating, and...
