Version 1
: Received: 20 June 2024 / Approved: 21 June 2024 / Online: 24 June 2024 (09:50:32 CEST)
How to cite:
Pedrosa, D.; Gaspar, I. Towards Universal Applied Supervised Machine Learning: A Multi-Agent Framework For Systematic Pipeline Executions. Preprints2024, 2024061554. https://doi.org/10.20944/preprints202406.1554.v1
Pedrosa, D.; Gaspar, I. Towards Universal Applied Supervised Machine Learning: A Multi-Agent Framework For Systematic Pipeline Executions. Preprints 2024, 2024061554. https://doi.org/10.20944/preprints202406.1554.v1
Pedrosa, D.; Gaspar, I. Towards Universal Applied Supervised Machine Learning: A Multi-Agent Framework For Systematic Pipeline Executions. Preprints2024, 2024061554. https://doi.org/10.20944/preprints202406.1554.v1
APA Style
Pedrosa, D., & Gaspar, I. (2024). Towards Universal Applied Supervised Machine Learning: A Multi-Agent Framework For Systematic Pipeline Executions. Preprints. https://doi.org/10.20944/preprints202406.1554.v1
Chicago/Turabian Style
Pedrosa, D. and Iago Gaspar. 2024 "Towards Universal Applied Supervised Machine Learning: A Multi-Agent Framework For Systematic Pipeline Executions" Preprints. https://doi.org/10.20944/preprints202406.1554.v1
Abstract
This paper presents MADS (Multi-Agents for Data Science), an innovative and simple open-source multi-agent framework designed for systemic pipeline execution in applied supervised machine learning. By leveraging the capabilities of multi-agent systems (MAS), we introduce a universal approach to optimize and streamline machine learning pipelines. Our framework highlights the differences between various types of agents, such as reinforcement learning (RL) agents and large language model (LLM) agents, and their distinct contributions to the process. While we currently employ LLM agents to automate and enhance machine learning tasks, we acknowledge the potential of incorporating RL agents in future iterations to further improve performance and adaptability. The primary objective is to enhance the efficiency, scalability, and adaptability of supervised learning applications across various domains. This integration addresses the complexity and manual effort typically associated with machine learning workflows, paving the way for more automated, robust, and scalable solutions. Our approach demonstrates significant improvements in task automation, reduced human intervention, and enhanced model performance. The MADS framework, which will soon be available as an open-source implementation, represents a pivotal contribution to the field of machine learning, promising to facilitate broader adoption and collaborative advancement.
Keywords
Multi-Agent Systems (MAS),Machine Learning Pipelines,Supervised Learning,Data Science Automation, Large Language Models (LLM), Reinforcement Learning (RL), Time Series Forecasting,AI Agents, Open-Source Framework,Model Efficiency, Task Automation·Scalability in Machine Learning, Model Adaptability, Collaborative AI Systems, Machine Learning Workflow Optimization
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
