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Home News Charmed Kubeflow 1.6 is now available from Canonical

Charmed Kubeflow 1.6 is now available from Canonical

by Jessica Weisman-Pitts
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The latest release of Canonical’s end-to-end MLOps platform brings advanced AI/ML training capabilities

8 September 2022- Canonical, the publisher of Ubuntu, announces today the release of Charmed Kubeflow 1.6,  an end-to-end MLOps platform with optimised complex model training capabilities.  Charmed Kubeflow is Canonical’s enterprise-ready distribution of Kubeflow, an open source machine learning toolkit designed for use with Kubernetes.

Charmed Kubeflow 1.6 follows the same release cadence as the Kubeflow upstream project. This latest version follows the Kubeflow roadmap and comes with performance enhancements and more advanced model training capabilities.

Speed up digital transformation: take models from concept to production 

Charmed Kubeflow helps data science teams automate tasks and boost productivity,  helping companies lower costs. The platform’s components use charms – Kubernetes operators that automate maintenance and security operations. Charms accelerate workload deployment,  allowing data scientists to take models to market more efficiently.

According to a 2022 IBM Index AI Report,  adoption of AI/ML within enterprises reached 35% last year. The benefits are clear. For instance, banks applying ML to build recommendation earnings have boosted sales by 10%, saved 20% in CAPEX, increased cash collections by 20%, and reduced churn by 20%.  Identifying tools that bring automation to data science workflows has become essential to getting a faster return on investment from AI/ML projects.

Improved data processing and tracking with MLOps

Besides accelerating deployments, Charmed Kubeflow 1.6 makes data processing more seamless.  Kubeflow’s 2022 survey reveals that data processing and transformation are the enterprise’s most challenging and time-consuming activities. Moreover, data comes from various sources, each with particular processes and dependencies.

Charmed Kubeflow 1.6 comes with better tracking capabilities.   AI/ML models can be measured more effectively;  evolution and debugging become simpler. The solution detects data drift and allows models to adapt to it quickly. Charmed Kubeflow 1.6 brings improved tracking for trial logs as well, allowing for efficient debugging in case of data source failure.

Optimised AI training and modelling

Models need up to 15 iterations to become production-ready, and only half of them get that far into the AI/ML journey. Charmed Kubeflow 1.6  supports population-based training (PBT), accelerating model iteration and improving the likelihood that models will reach production readiness. An MPI operator makes training large volumes of data more efficient. PyTorch elastic training enhancements make model training more effective and help ML engineers get started quickly.

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Jessica Weisman-Pitts

Jesse Pitts has been with the Global Banking & Finance Review since 2016, serving in various capacities, including Graphic Designer, Content Publisher, and Editorial Assistant. As the sole graphic designer for the company, Jesse plays a crucial role in shaping the visual identity of Global Banking & Finance Review. Additionally, Jesse manages the publishing of content across multiple platforms, including Global Banking & Finance Review, Asset Digest, Biz Dispatch, Blockchain Tribune, Business Express, Brands Journal, Companies Digest, Economy Standard, Entrepreneur Tribune, Finance Digest, Fintech Herald, Global Islamic Finance Magazine, International Releases, Online World News, Luxury Adviser, Palmbay Herald, Startup Observer, Technology Dispatch, Trading Herald, and Wealth Tribune.