# LoCoML: A Framework for Real-World ML Inference Pipelines

arXiv: https://arxiv.org/abs/2501.14165 · HTML: https://mlsanity.com/paper/2501.14165/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 10 of 10)

- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [A Software Engineering Perspective on Engineering Machine Learning Systems: State of the Art and Challenges](https://mlsanity.com/paper/2012.07919/) (2020)
- [Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows](https://mlsanity.com/paper/2101.04834/) (2021)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Software Architecture for ML-based Systems: What Exists and What Lies Ahead](https://mlsanity.com/paper/2103.07950/) (2021)
- [An Artificial Intelligence Life Cycle: From Conception to Production](https://mlsanity.com/paper/2108.13861/) (2021)
- [Trinity: A No-Code AI platform for complex spatial datasets](https://mlsanity.com/paper/2106.11756/) (2021)
- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
- [End-User Development for Artificial Intelligence: A Systematic Literature Review](https://mlsanity.com/paper/2304.09863/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search](https://mlsanity.com/paper/2507.00004/) (2025)
- [From Intent to AI Pipelines: A Controlled Agentic Framework for Non-AI Expert Scientists](https://mlsanity.com/paper/2605.18764/) (2026)

## Abstract (arXiv)

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 textitBhashini 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.