LoCoML: A Framework for Real-World ML Inference Pipelines

• URL: http://arxiv.org/abs/2501.14165v1
• Date: Fri, 24 Jan 2025 01:35:08 GMT
• Title: LoCoML: A Framework for Real-World ML Inference Pipelines
• Authors: Kritin Maddireddy, Santhosh Kotekal Methukula, Chandrasekar Sridhar, Karthik Vaidhyanathan,
• Abstract summary: LoCoML is a low-code framework designed to simplify the integration of diverse machine learning models. We show that LoCoML adds only a small amount of computational load, making it efficient and effective for large-scale ML integration.
• Score: 0.0
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• Abstract: 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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