Will generative AI designed for the enterprise (AI that auto-completes formulas in reports, spreadsheets, etc.) ever become interoperable? Together with related organizations like Cloudera and Intel, we are supporting and supporting a growing number of open source efforts. The Linux Foundation, a nonprofit organization that sustains it, aims to find out.
The Linux Foundation today announced the launch of Open Platform for Enterprise AI (OPEA). This is a project that fosters the development of open, multi-provider, configurable (i.e. modular) generative AI systems. Under the Linux Foundation’s LFAI and Data organization, which focuses on AI and data-related platform initiatives, OPEA’s goal is to pave the way for the release of “enhanced” and “scalable” generative AI systems. is. It is the best open source innovation from across the ecosystem,” said Ibrahim Haddad, director of LFAI and his data executive, in a press release.
“OPEA will unlock new possibilities for AI by creating a detailed, configurable framework at the forefront of the technology stack,” Haddad said. “This initiative is a testament to our mission to advance open source innovation and collaboration within the AI and data community under a neutral and open governance model.”
In addition to Cloudera and Intel, OPEA, one of the Linux Foundation’s sandbox projects and an incubator program, includes companies such as Intel, IBM-owned Red Hat, Hugging Face, Domino Data Lab, MariaDB, and VMWare. Its members include leading companies.
So what exactly can they build together? Haddad calls for “optimized” support for AI toolchains and compilers that allow AI workloads to run across different hardware components. , and a “heterogeneous” pipeline for search augmentation generation (RAG).
RAGs are becoming increasingly popular in enterprise applications of generative AI, and it’s not hard to see why. Most generative AI models’ answers and actions are limited to the data used to train them. However, RAGs allow you to extend the model’s knowledge base to information beyond the original training data. The RAG model references this external information (which may take the form of company-proprietary data, public databases, or a combination of the two) before generating a response or performing a task.
A diagram explaining the RAG model.
Intel provided further details in its own document press release:
Companies are taking on a do-it-yourself approach [to RAG] This is because there is no de facto standard across components that allows companies to select and deploy RAG solutions that are open, interoperable, and help them get to market quickly. OPEA plans to address these issues by working with industry to standardize components such as frameworks, architectural blueprints, and reference solutions.
Evaluation will also be an important part of OPEA’s efforts.
Inside GitHub repositoryOPEA proposes a rubric for evaluating generative AI systems along four axes: performance, functionality, reliability, and “enterprise-grade” readiness. performance As defined by OPEA, this involves “black box” benchmarking from real-world use cases. Features It evaluates a system’s interoperability, deployment options, and ease of use. Trustworthiness focuses on the “robustness” and ability to ensure the quality of an AI model.and Ready for the enterprise It focuses on the requirements to get the system up and running without major issues.
Rachel Roumeliotis, director of open source strategy at Intel, said: To tell OPEA works with the open source community to provide rubric-based testing and, upon request, evaluation and grading of generative AI deployments.
OPEA’s other initiatives are still undecided at this time. However, Haddad hinted at the possibility of open model development along the lines of his expanding Llama family at Meta and his DBRX at Databricks. To that end, Intel has already provided reference implementations of generative AI-powered chatbots, document summarizers, and code generators optimized for Xeon 6 and Gaudi 2 hardware in his OPEA repository. I am.
Currently, OPEA members are clearly invested (and self-serving, for that matter) in building tools for enterprise-generated AI. Recent Cloudera We have started a partnership This is to build the “AI ecosystem” proposed by the company on the cloud. Domino’s offers series of apps Aimed at building and auditing generative AI for business.And VMWare, aimed at the infrastructure side of enterprise AI, was released last August. New “private AI” computing products.
The question is how these vendors will respond under OPEA. actually Do you want to collaborate to build cross-compatible AI tools?
There are clear advantages to doing so. Customers are willing to use multiple vendors depending on their needs, resources, and budget. But as history has shown, it’s very easy to fall into vendor lock-in. I hope that’s not the final outcome.