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!
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.
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.
Our original Kubernetes tool list was so popular that we've curated another great list of tools to help you improve your functionality with the platform.
You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
How To Plot A Decision Boundary For Machine Learning Algorithms in Python, you will discover how to plot a decision surface for a classification machine learning algorithm.
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Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives. It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.