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Will generative AI designed for enterprises (e.g., AI that autocompletes formulas in reports and spreadsheets) ever become interoperable? Supporting growing open source efforts with related organizations like Cloudera and Intel and the Linux Foundation, a nonprofit organization that maintains it, aims to find out.
The Linux Foundation on Tuesday announced the launch of the Open Platform for Enterprise AI (OPEA), a project to accelerate the development of open, multi-provider, configurable (i.e., modular) generative AI systems. Under the Linux Foundation's LF AI and Data organization, which focuses on AI and data-related platform initiatives, OPEA's goal paves the way for the release of “enhanced” and “scalable” generative AI systems. That's it. This is the best open source innovation from across the ecosystem,” Ibrahim Haddad, executive director of LF AI and Data, said 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, a sandbox project of the Linux Foundation and an incubator program of sorts, includes companies such as Intel, IBM-owned Red Hat, Hugging Face, Domino Data Lab, MariaDB, and VMware. It includes leading companies as members.
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.
Intel provided some more details in its own 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.
In its GitHub repository, OPEA 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. reliability We focus on the ability to ensure the “robustness” and quality of AI models.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 OPEA will work with the open source community to provide rubric-based testing and evaluation and grading of generative AI deployments upon request. .
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 .
Currently, OPEA members are clearly invested (and self-serving, for that matter) in building tools for enterprise-generated AI. Cloudera recently launched a partnership to build what it calls an “AI ecosystem” in the cloud. Domino provides a set of apps for building and auditing generative AI for your business. And VMware, leaning toward the infrastructure side of enterprise AI, announced a new “private AI” computing product last August.
The question is whether these vendors will do so. actually Collaborate to build mutually compatible AI tools under OPEA.
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.