Simplifying on-premises Machine Learning infrastructure with MLAnywhere


In our previous blog, we looked at how public clouds have set the pace and standards for satisfying the technology needs of data scientists, and how on-premises offerings have become increasingly attractive due to innovations such as Kubernetes and Kubeflow. Nevertheless, delivery of ML platforms on-premises is still not easy. The effort to replicate a public cloud ML experience requires enthusiasm and persistence in the face of potential frustration. To address this challenge, the Cisco community has developed an open source tool named MLAnywhere to assist IT teams in learning and mastering the new technology stacks that ML projects require. MLAnywhere provides an actual, usable outcome in the form of a deployed Kubeflow workflow (pipeline) with sample ML applications on top of Kubernetes via a clean and intuitive interface.

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