# A Declarative Query Language for Scientific Machine Learning

arXiv: https://arxiv.org/abs/2405.16159 · HTML: https://mlsanity.com/paper/2405.16159/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 76 references that are in the map.
- First posted on arXiv 25 May 2024.

## Builds on (most cited first, 12 of 12)

- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks](https://mlsanity.com/paper/2304.14979/) (2023)
- [FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis](https://mlsanity.com/paper/2401.10506/) (2024)
- [HuntGPT: Integrating Machine Learning-Based Anomaly Detection and Explainable AI with Large Language Models (LLMs)](https://mlsanity.com/paper/2309.16021/) (2023)
- [SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle](https://mlsanity.com/paper/1909.02976/) (2019)
- [The Cost of Training Machine Learning Models over Distributed Data Sources](https://mlsanity.com/paper/2209.07124/) (2022)
- [Using Large Language Models to Automate and Expedite Reinforcement Learning with Reward Machine](https://mlsanity.com/paper/2402.07069/) (2024)
- [sql4ml A declarative end-to-end workflow for machine learning](https://mlsanity.com/paper/1907.12415/) (2019)
- [Declarative Machine Learning - A Classification of Basic Properties and Types](https://mlsanity.com/paper/1605.05826/) (2016)
- [P6: A Declarative Language for Integrating Machine Learning in Visual Analytics](https://mlsanity.com/paper/2009.01399/) (2020)
- [EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications](https://mlsanity.com/paper/2403.18203/) (2024)

## Abstract (arXiv)

The popularity of data science as a discipline and its importance in the emerging economy and industrial progress dictate that machine learning be democratized for the masses. This also means that the current practice of workforce training using machine learning tools, which requires low-level statistical and algorithmic details, is a barrier that needs to be addressed. Similar to data management languages such as SQL, machine learning needs to be practiced at a conceptual level to help make it a staple tool for general users. In particular, the technical sophistication demanded by existing machine learning frameworks is prohibitive for many scientists who are not computationally savvy or well versed in machine learning techniques. The learning curve to use the needed machine learning tools is also too high for them to take advantage of these powerful platforms to rapidly advance science. In this paper, we introduce a new declarative machine learning query language, called em MQL, for naive users. We discuss its merit and possible ways of implementing it over a traditional relational database system. We discuss two materials science experiments implemented using MQL on a materials science workflow system called MatFlow.