Canonical/Ubuntu: Charmed Kubeflow 1.7, iKOOLCORE R1, and Ubuntu Pro (UPDATED)
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Ubuntu Blog: Charmed Kubeflow 1.7 is now available
Run serverless ML workloads. Optimise models for deep learning. Expand your data science tooling.
Canonical, the publisher of Ubuntu, announced today the general availability of Charmed Kubeflow 1.7. Charmed Kubeflow is an open-source, end-to-end MLOps platform that can run on any cloud, including hybrid cloud or multi-cloud scenarios. This latest release offers the ability to run serverless machine learning workloads and perform model serving, regardless of the framework that professionals use. This new capability increases developer productivity by reducing routine tasks, helping organisations lower their operational costs. It unburdens developers from explicitly describing the infrastructure underneath.
Based on a poll run by Canonical, open source and ease of use are the most important factors professionals consider when selecting AI/ML tooling. Charmed Kubeflow 1.7 expands its spectrum of open-source frameworks and libraries and makes the model development deployment process easier with a new set of capabilities.
Serverless workloads and new model serving capabilities
In a recent MLOps report by Deloitte AI Institute, 74% of respondents indicated that they plan to integrate artificial intelligence (AI) into all enterprise applications within three years. To achieve this, companies need to find ways to scale their AI projects in a reproducible, portable and reliable manner. Charmed Kubeflow 1.7 brings new capabilities for enterprise AI:
- The introduction of KNative in the Kubeflow bundle allows organisations to run serverless machine learning workloads.
- The addition of KServe enables users to perform model serving, regardless of the framework.
- New frameworks for model serving, such as NVIDIA Triton.
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iKOOLCORE R1 review – A quad 2.5GbE mini PC tested with Windows 11, Ubuntu 22.04, Proxmox
When I first saw the iKOOLCORE R1 I was fascinated that a mini PC of similar size to the smallest fully functional ones available (think Chuwi LarkBox, GMK NucBox or ECS LIVA Q Series) could be equipped with four 2.5 gigabit Ethernet (2.5GbE) ports. I approached iKOOLCORE who kindly provided an R1 for review and I’ve looked at performance running both Windows 11 and Ubuntu 22.04 and dabbled with using hypervisors on this mini PC through Proxmox virtual environment. iKOOLCORE R1 specifications iKOOLCORE list the R1 specifications on their website as: Of note are the ‘EC, FCC, RoHS’ certifications indicating both European conformity and approval for use in the US.
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Ubuntu Pro Tackles the Challenge of Enterprise Open Source Adoption
Attractive as open source is, many organizations still have concerns. With these challenges in mind, Canonical, released Ubuntu Pro, a comprehensive subscription for open source security, compliance, and support.
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Ninety-seven percent of applications leverage open source code, and 90% of companies are applying or using it in some way. According to Forrester, more than half of Fortune 500 companies use open source software for their development projects. In 2022, developers started 52 million new open source projects on GitHub. And, developers across the platform made more than 413 million contributions to open source projects.
And yet, attractive as open source is, many organizations still struggle with the “how.” Concerns over support, security, and compliance continue to hover over open source adoption. Those certainly are areas where no enterprise can afford to compromise.
With these challenges in mind, Canonical, the publisher of Ubuntu, recently released Ubuntu Pro, a comprehensive subscription for open source security, compliance, and support.
UPDATE:
More on Kubeflow 1.7:
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Open source Kubeflow 1.7 set to ‘transform’ MLops
Kubeflow 1.7 became generally available today (March 29), providing the first update to the widely used open source MLops platform since the debut of Kubeflow 1.6 in Sept. 2022. At its core, Kubeflow is an open source ML toolkit that helps organizations to deploy and run ML workflows on cloud-native Kubernetes infrastructure. Among the themes of the Kubeflow 1.7 update is a focus on helping to better support transformer based models.