Building ML Componentes on Kubernetes

Building ML Componentes on Kubernetes

Building Machine learning components on Kubernetes. Here at KONA, everything we build is a component, and that concept has changed us, the way we work, and our product strategy!

Hello!

Here at KONA, everything we build is a component, and that concept has changed us, the way we work, and our product strategy!

Each time we discuss a product feature or a new product, we first think of what we need and what components we can re-use from what we already have in our Catalog.

How we manage to do that

Every project or feature starts with a Jupyter notebook or directly with python code, in either way, we can package that into a component (Pod) and deploy it to our cluster to train and serve.

Assuming the most complex scenery (starting with Jupyter), we can have our notebook on the left side, then the building process with Fairing, train, and serving.

Kubeflow Fairing streamlines the process of building, training, and deploying machine learning (ML) training jobs in Kubernetes. By using Fairing and adding a few code lines, you can run your ML training job locally or in the cloud, directly from Python code or a Jupyter notebook. After your training job is complete, you can use Kubeflow Fairing to deploy your trained model as a prediction endpoint.

And then you start from an idea, test it in a notebook and deploy to a scalable cluster on Kubernetes with serving, auto-scale.

python machine-learning ai kubernetes kubeflow

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