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Kubeflow 1.0

Submitted by Roy Schestowitz on Tuesday 3rd of March 2020 01:46:11 AM Filed under
Server
Software
  • Kubeflow 1.0 launches

    Kubeflow is an open source artificial intelligence / machine learning (AI/ML) tool that helps improve deployment, portability and management of AI/ML models. This tool allows users to quickly create, train and tune neural networks within Kubernetes for dynamic resource provisioning. It helps data scientists and AI/ML developers who want to easily setup a project or pipeline by unifying all the required tools under one installation. Kubeflow works well with TensorFlow and other modern AI/ML frameworks such as PyTorch, MXNet and Chainer allowing users to enhance their existing code and setup.

  • Kubeflow 1.0: An open source journey towards end-to-end enterprise machine learning
»

Kubeflow 1.0 Released- A cloud native Machine Learning

Submitted by Roy Schestowitz on Wednesday 4th of March 2020 05:18:38 PM.
  • Kubeflow 1.0 Released- A cloud native Machine Learning

    Kubeflow 1.0: Thea Lamkin from Kubeflow recently said, “On behalf of the entire community, we are proud to announce Kubeflow 1.0, our first major release”. She added that It was open-sourced at Kubecon USA before two years and the project has gone beyond their wildest expectations. The goal of this project is to make machine learning easy for engineers, and data scientists to leverage cloud assets.

First Major Release of Kubeflow Machine Learning Toolkit...

Submitted by Roy Schestowitz on Monday 9th of March 2020 04:14:49 AM.

Kubeflow 1.0 Advances AI Adoption on Kubernetes Platforms

Submitted by Roy Schestowitz on Monday 9th of March 2020 04:28:27 AM.
  • Kubeflow 1.0 Advances AI Adoption on Kubernetes Platforms

    An open source set of Kubeflow tools designed to make it easier to build and deploy machine learning models on Kubernetes clusters has formally reached a 1.0 milestone.

    [...]

    Elements of Kubeflow that have been included in the 1.0 release include a user interface, Jupyter notebook controller and web application, Tensorflow Operator (TFJob) and PyTorch Operator for distributed training, kfctl for deployment and upgrades, and a profile controller and UI for multiuser management. There is also an Operator framework designed to make it easier to deploy, manage and update Kubeflow on Kubernetes clusters.

  • Kubeflow 1.0 brings cloud-native machine learning to Kubernetes

    Kubeflow became open source software in December of 2017 at Kubecon USA. Now, in March of 2020, the first major release has arrived. Kubeflow 1.0 graduates several applications that help develop, build, train, and deploy models on Kubernetes.

    Kubeflow is a machine learning toolkit designed to make deploying scalable ML workflows on Kubernetes easier. It includes a custom TensorFlow training job operator and services for creating and deploying Jupyter notebooks and focuses on leveraging cloud assets.

    Let’s take a look at what the 1.0 milestone includes and what else Kubeflow has under the hood.

Open source machine learning platform Kubeflow reaches 1.0

Submitted by Roy Schestowitz on Monday 9th of March 2020 06:21:27 AM.
  • Open source machine learning platform Kubeflow reaches version 1.0

    Kubeflow, the freely available machine learning platform cofounded by developers at Google, Cisco, IBM, Red Hat, CoreOS, and CaiCloud, made its debut at the annual Kubecon conference in 2017. Three years later, Kubeflow has reached version 1.0 — its first major release — as the project grows to hundreds of contributors over 30 participating organizations. Companies including US Bank, Chase, GoJek, Amazon Web Services, Bloomberg, Uber, Shopify, GitHub, Canonical, Intel, Alibaba Cloud, TuSimple, Dell, Shell, Arrikto, and Volvo are among those using it in production.

    Project coauthors Jeremy Lewi, Josh Bottum, Elvira Dzhuraeva, David Aronchick, Amy Unruh, Animesh Singh, and Ellis Bigelow announced the news in a Medium post this morning. “Kubeflow’s goal is to make it easy for machine learning engineers and data scientists to leverage cloud assets (public or on-premise) for [machine learning] workloads,” they wrote. “With Kubeflow, there is no need for data scientists to learn new concepts or platforms to deploy their applications, or to deal with ingress, networking certificates, etc.”

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