Arrikto, the leader in machine learning on Kubernetes, participated in the announcement of Kubeflow 1.5, the latest version of the open source MLOps platform, with contributions from Google, Arrikto, IBM, Twitter and Rakuten, alongside numerous other contributors. Kubeflow 1.5 delivers lower infrastructure costs, and helps simplify the operation of the end-to-end machine learning platform. Originally developed by Google, Kubeflow is a complete MLOps toolkit, including integrated components for model development, model training, multi-step pipelines, AutoML, serving, monitoring, artifact management, and experiment tracking. Running production machine learning workflows at scale is notoriously expensive due to outsized requirements on CPUs, GPUs, storage, and memory. Kubeflow 1.5 introduces several key features to reduce these costs.

The Kubeflow project was announced back in December 2017 and has since become a very popular machine learning platform with both data scientists and MLOps engineers. If you are new to the Kubeflow ecosystem and community, here's a quick rundown. Kubeflow is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. In a nutshell, Kubeflow is the machine learning toolkit for Kubernetes. As such, anywhere you are running Kubernetes, you should also be able to run Kubeflow.

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Data scientists and engineers typically work in parallel to DevOps production pipelines when they create machine learning (ML) models. Through so-called MLOps, the ML models these teams build are often then integrated into the application-deployment environment once ready for production. These data scientists or MLOps teams might also rely on GitOps to serve as the immutable application structure with a Git repository so that the ML models are distributed across different environments, such as hybrid clouds. To facilitate the deployment of ML models and applications on Kubernetes, open source Kubeflow has emerged as one of the more promising open source tools to help with the process. Still, the challenges associated with MLOps teams deploying ML models and applications on Kubernetes -- even with tools such as Kubeflow -- are fraught with difficulty and complexity.

Kubeflow is an open-source project, dedicated to making deployments of ML projects simpler, portable and scalable. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. But how do we get started? Do we need a Kubernetes cluster?