[2410.20791] From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap

[2410.20791] From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap

arXiv - AI 4 min read

About this article

Abstract page for arXiv paper 2410.20791: From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap

Computer Science > Software Engineering arXiv:2410.20791 (cs) [Submitted on 28 Oct 2024 (v1), last revised 6 Apr 2026 (this version, v3)] Title:From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap Authors:Gopi Krishnan Rajbahadur, Gustavo A. Oliva, Dayi Lin, Jiho Shin, Ahmed E. Hassan View a PDF of the paper titled From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap, by Gopi Krishnan Rajbahadur and 4 other authors View PDF HTML (experimental) Abstract:The rapid expansion of foundation models (FMs), such as large language models (LLMs), has given rise to FMware, software systems that integrate FM(s) as core components. While building demonstration-level FMware is relatively straightforward, transitioning to production-ready systems presents numerous challenges, including reliability, high implementation costs, scalability, and compliance with privacy regulations. Our paper conducts a semi-structured thematic synthesis to identify key challenges in productionizing FMware across diverse data sources, including our industry experience developing FMArts, a FMware lifecycle engineering platform, and its integration into Huawei Cloud; grey literature; academic publications; hands-on involvement in the Open Platform for Enterprise AI (OPEA); organizing the AIware conference and bootcamp; and co-leading the ISO SPDX SBOM working group on AI and datasets. We identify critical issues in FM(s) selection, data and model a...

Originally published on April 08, 2026. Curated by AI News.

Related Articles

[2603.16105] Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization
Llms

[2603.16105] Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization

Abstract page for arXiv paper 2603.16105: Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization

arXiv - AI · 4 min ·
[2603.09643] MM-tau-p$^2$: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Control Settings
Llms

[2603.09643] MM-tau-p$^2$: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Control Settings

Abstract page for arXiv paper 2603.09643: MM-tau-p$^2$: Persona-Adaptive Prompting for Robust Multi-Modal Agent Evaluation in Dual-Contro...

arXiv - AI · 4 min ·
[2603.07339] Agora: Teaching the Skill of Consensus-Finding with AI Personas Grounded in Human Voice
Llms

[2603.07339] Agora: Teaching the Skill of Consensus-Finding with AI Personas Grounded in Human Voice

Abstract page for arXiv paper 2603.07339: Agora: Teaching the Skill of Consensus-Finding with AI Personas Grounded in Human Voice

arXiv - AI · 4 min ·
[2602.00185] QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities
Llms

[2602.00185] QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities

Abstract page for arXiv paper 2602.00185: QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities

arXiv - AI · 4 min ·
More in Llms: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime