LoCoML简化多模型集成，助力20+语言的AI应用落地
LoCoML: A Framework for Real-World ML Inference Pipelines
- 低代码框架连接异构机器学习模型，支持多语言技术协同
- 在大项目中仅引入少量计算开销，保持高效运行
- 适合需要跨团队协作的大型人工智能系统集成
机器学习的广泛应用带来了架构和数据需求各异的多样化模型，给实际应用集成带来新挑战。传统方案难以有效连接异构模型，尤其在多方参与的大规模协作项目中问题更突出。为此，我们开发了LoCoML——一个低代码框架，用于简化在 extit{Bhashini项目}（一项旨在整合自动语音识别、机器翻译、文语转换、光学字符识别等语言技术，支持超过20种语言无缝沟通的大型计划）背景下多种机器学习模型的集成。初步评估表明，LoCoML仅引入极小的计算开销，具备高效性与可扩展性。实践表明，低代码方法是实现多模型协作集成的可行路径。
原文摘要 · Abstract (English)
The widespread adoption of machine learning (ML) has brought forth diverse models with varying architectures, and data requirements, introducing new challenges in integrating these systems into real-world applications. Traditional solutions often struggle to manage the complexities of connecting heterogeneous models, especially when dealing with varied technical specifications. These limitations are amplified in large-scale, collaborative projects where stakeholders contribute models with different technical specifications. To address these challenges, we developed LoCoML, a low-code framework designed to simplify the integration of diverse ML models within the context of the \textit{Bhashini Project} - a large-scale initiative aimed at integrating AI-driven language technologies such as automatic speech recognition, machine translation, text-to-speech, and optical character recognition to support seamless communication across more than 20 languages. Initial evaluations show that LoCoML adds only a small amount of computational load, making it efficient and effective for large-scale ML integration. Our practical insights show that a low-code approach can be a practical solution for connecting multiple ML models in a collaborative environment.
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