InfraMLForge: Developer Tooling for Rapid LLM Development and Scalable Deployment

Large language models (LLMs) have transformed the landscape of artificial intelligence, presenting immense potential across various applications. However, the complexity involved in developing and deploying these models can hinder progress for many practitioners. To address this challenge, we introduce InfraMLForge, a comprehensive ecosystem that serves as a toolkit for developers. InfraMLForge is tailored to streamline the development process, allowing machine learning practitioners to prioritize innovation while alleviating infrastructure burdens. The toolkit features essential components such as model training utilities, dataset management capabilities, and versatile deployment pipelines optimized for cloud and edge environments. This integration facilitates smooth transitions throughout the development lifecycle and ensures thorough monitoring of models post-deployment. Through various case studies, we illustrate the operational effectiveness of InfraMLForge and its capacity to enhance developer productivity while supporting a range of workloads, effectively positioning it as a vital resource for advancing LLM applications.

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Published
2025-07-11
DOI
https://doi.org/10.1109/cisat66811.2025.11181815
Primary Topic
Scientific Computing and Data Management
Type
article
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article

InfraMLForge: Developer Tooling for Rapid LLM Development and Scalable Deployment

Yuhan Zhang
Scientific Computing and Data Management
article

InfraMLForge: Developer Tooling for Rapid LLM Development and Scalable Deployment

Yuhan Zhang
article en

Abstract

Large language models (LLMs) have transformed the landscape of artificial intelligence, presenting immense potential across various applications. However, the complexity involved in developing and deploying these models can hinder progress for many practitioners. To address this challenge, we introduce InfraMLForge, a comprehensive ecosystem that serves as a toolkit for developers. InfraMLForge is tailored to streamline the development process, allowing machine learning practitioners to prioritize innovation while alleviating infrastructure burdens. The toolkit features essential components such as model training utilities, dataset management capabilities, and versatile deployment pipelines optimized for cloud and edge environments. This integration facilitates smooth transitions throughout the development lifecycle and ensures thorough monitoring of models post-deployment. Through various case studies, we illustrate the operational effectiveness of InfraMLForge and its capacity to enhance developer productivity while supporting a range of workloads, effectively positioning it as a vital resource for advancing LLM applications.

Northeastern University (US)
Openalex Percentile: Top 74%
Scientific Computing and Data Management
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InfraMLForge: Developer Tooling for Rapid LLM Development and Scalable Deployment — Yuhan Zhang · (2025) | TGRS Research Map | TGRS