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IBM/Red Hat: Red Hat as 'Community' and PR Fluff, Watson (Proprietary) Promotion

Submitted by Roy Schestowitz on Monday 16th of November 2020 11:42:10 PM Filed under
Red Hat
  • Red Hat Learning Community celebrates 50,000 members

    The Red Hat Learning Community (RHLC) celebrates a monumental milestone this week as it exceeds 50,000 members! At its launch in September of 2018, the main goal was simple: provide a collaborative space for open source learners to connect as they optimize their skills in working with Red Hat products. As the core of that mission has remained true since launch, Red Hat has strived to provide our users what they need in order to collaborate, learn, build skills, and meet their individual learning and career goals.

  • Log-On Wave for IBM Z simplifies highly virtualized environments [Ed: So-called 'journalism' that's just a rewrite of the press release, i.e. an ad]

    IBM business partner Log-On Software has announced Log-On Wave for IBM Z to simplify and accelerate the management and daily administration of highly virtualized Linux server environments on IBM Z and IBM LinuxONE.

    “IBM Z and IBM LinuxOne are powerful, reliable and economical platforms for highly virtualized Linux environments. Significant z/VM skills are required to monitor, manage, and provision Linux instances and resources,” said Log-On.

  • Log-On Software Announces Log-On Wave for IBM Z

    Log-On Wave for IBM Z simplifies and accelerates the administration and operation of highly virtualized Linux infrastructures on IBM Z and LinuxONE

    [...]

    IT organizations and service providers need to quickly respond to changing business requirements by rapidly deploying virtual resources and diagnosing and resolving system issues. Log-On Wave for IBM Z accelerates operations and reduces friction, ensuring that service level agreements are met, and customer expectations are exceeded.

  • Kubeflow Pipelines meets Tekton and Watson

    More machine learning models need to be deployed in production in a faster, repeatable, and consistent manner — and with the right governance.

    In March 2018 at the Strata Data Conference, IBM VP Dinesh Nirmal noted a common refrain) in the machine learning echelon, “The story of enterprise Machine Learning — It took me 3 weeks to develop the model. It’s been >11 months, and it’s still not deployed.”

    Echoing this sentiment, Forrester states in their 2020 report, “A top complaint of data science, application development and delivery (AD&D) teams, and, increasingly, line-of-business leaders is the challenge in deploying, monitoring, and governing machine learning models in production. Manual handoffs, frantic monitoring, and loose governance prevent organizations from deploying more AI use cases.”

    [...]

    The decision to adopt Kubeflow Pipelines on our side came with an internal requirement to redesign Kubeflow Pipelines to run on top of Tekton (a Kubernetes-native CI/CD engine) instead of Argo. Tekton provides Kubernetes-style resources for declaring CI/CD-style pipelines, and introduces several new Custom Resource Definitions (CRDs) including Task, Pipeline, TaskRun, and PipelineRun. Within IBM, we have standardized on Tekton as a cloud CI/CD engine, and OpenShift Pipelines is based on Tekton. Additionally, Tasks in Tekton can be managed and executed independently of pipelines, which is valuable for us.

    Given the strategic and technical alignment with Tekton, it was a natural fit for our team to rewrite and run Kubeflow Pipelines on top of Tekton. As we set out to design and execute this work, we got support great support through Google Kubeflow Pipelines team, CD Foundation MLOps Sig, and Red Hat. After an extensive effort, we have Kubeflow Pipelines running on Tekton end-to-end and available in open source. Read our design document to understand our process and requirements.

»

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