The Linux Foundation’s Artificial Intelligence Community Announces New Acumos Release Focused on Creation of AI/ML Models

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The LF AI Foundation, the organization building an open AI community to drive open source innovation in artificial intelligence (AI), machine learning (ML) and deep learning (DL), announced the new release of Acumos code named Boreas. This latest release of the open source framework and marketplace will enable the creation, training and license verification of AI, ML and DL models and apps, among other benefits to the community of developers and data scientists.

“The technology industry is on the precipice of a major technological shift with AI, which is exactly the point in any technology evolution where open source software and community can accelerate development,” said Ibrahim Haddad, executive director of LF AI Foundation. “An open source framework that is iterated upon early and often is key in transforming the development work for data scientists and developers, and Acumos is that foundation for innovation.

Acumos AI is a platform and open source framework that makes it easy to build, share, and deploy AI apps. Acumos standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment. This frees data scientists and model trainers to focus on their core competencies and accelerates innovation

Acumos is part of the LF AI Foundation, an umbrella organization within The Linux Foundation that supports and sustains open source innovation in AI, ML, and DL while striving to make these critical new technologies available to developers and data scientists everywhere

The latest Acumos AI release includes:

  • Support for onboarding of ONNX, PFA and Dockerized models.
  • Enhanced Acumos platform peering through a controlled process of partner catalog publication and subscription: (i) global catalog search capability, (ii) federation of catalogs.
  • Support for AI/ML model suppliers to provide a commercial software license with their models in the Acumos marketplace: (i) security scans of license metadata for models (Disabled with Security Verification turned off), (ii) support verification of licenses and Right-To-Use for commercial models, (iii) logging to enable model activity tracking and reporting.
  • Support for ML Workbench to allow the creation and training of AI/ML models in Acumos platform: (i) support for notebooks development environment (Jupyter), (ii) support for pipeline (NiFi) tools are integrated with Acumos (NiFi Pipeline tools are available as a Beta Feature only under Kubernetes).
  • Enhanced user experience in portal: publishing, unpublishing, deploying , onboarding, model building, and chaining, etc.
  • Enhanced logging standards: (i) log formats aligned with ONAP, (ii) support for log management tools.
  • Enhanced support for deploying Acumos platform under Kubernetes

“Acumos Boreas represents a significant next step in open source AI and machine learning,” said Dr Ofer Hermoni, Director of Product Strategy at Amdocs and Chair of the LF AI Technical Advisory Counsel. “With the ability to create and train models, we’re well on our way to providing
all the essential tools for rapid innovation for data scientists and developers building AI and machine learning apps.”

Global Adoption of Acumo

Integration, adoption and deployment of Acumos around the world is well underway and demonstrates momentum for a common, open framework to accelerate innovation in the AI, ML and DL app space. Two key examples are Orange and Tech Mahindra. Orange is using Acumos for an AI Marketplace and is integrating the upcoming Acumos Clio release with ONAP in order to test it on ONAP OpenLab and the 5G research platform Plug’in. Orange’s contribution to the Acumos includes the Onboarding enhancements seen in Acumos Boreas. Tech Mahindra is integrating Acumos into a number of its initiatives. TechMahindra GAiA is the first enterprise-grade open source AI platform, hosting a marketplace of AI models for a wide group of industry verticals. These are used as the basis for building, sharing and rapidly deploying AI-driven services and applications to solve business critical problems.

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