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Artificial Intelligence

Kubernetes releases Kubeflow: a scalable and portable machine learning toolkit

Kubernetes releases Kubeflow: a scalable and portable machine learning toolkit

Kubernetes community has introduced a new open source project called Kubeflow which makes the use of machine learning stacks on Kubernetes platform easy, fast and extensible.

Kubeflow project contains JupyterHub, Tensorflow Custom Resource (CRD), and a Tensorflow Serving container, which makes it the hybrid solution, and deployment platform of choice for machine learning.

JupyterHub

It is used to develop open-source software and services for interactive computing across dozens of programming languages. Kubeflow has integrated JupyterHub for creating and managing interactive Jupyter notebooks.

The Jupyter notebooks enables creation and sharing of documents that contain live code, equations, visualizations, etc. Hence, Kubeflow will help the data science and research groups performing on a range of tasks from data cleaning, numerical simulation to machine learning and more.

Tensorflow Custom Resource (CRD)

Kubeflow users can configure CRD to use CPUs or GPUs, and adjust to a specific cluster size with a single setting.

“This project marks the beginning of the end of the data scientist and/or software engineer as disparate roles,” said Philip Winder, an engineer and consultant at Container Solutions. “Like DevOps has merged operations and development, DataDevOps will consume data science.”

Tensorflow Serving container

It is a flexible and high-performance serving system used for machine learning models, and makes the deployment of new algorithms and experiments easier, without changing the server architecture and APIs.

Ksonnet

Additionally, Kubeflow uses Ksonnet project, the system to solve some tough service management problems regarding the deployment of container based applications.

Integrated with Kubeflow, Ksonnet will enable Kubernetes users to move workloads between multiple environments (development, test, and production).

“The Kubeflow project was a needed advancement to make it significantly easier to set up and productionize machine learning workloads on Kubernetes, and we anticipate that it will greatly expand the opportunity for even more enterprises to embrace the platform. We look forward to working with the project members in providing tight integration of Kubeflow with Tectonic, the enterprise Kubernetes platform.”Reza Shafii, VP of product, CoreOS

Also read: Artificial Intelligence can add $957 billion to Indian economy by 2035: Accenture report

Kubernetes community is still working on Kubeflow in collaboration with CaiCloud, Red Hat & OpenShift, Container Solutions, and many others.

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