This project introduces an intelligent framework. It automates end-to-end workflows of machine learning through joint AI agents. Each agent specializes in critical data load, target selection, preprocessing, exploratory analysis and model training to ensure systematic and interpretable model development. The Crewai-built system integrates Pydantic for verification, pandas for data processing and SCIKIT learning for modeling, providing efficiency and transparency.Major innovations include heuristic target selection, adaptive preprocessing, and self-study code generation. This framework reduces manual movement, ensures adaptation flexibility, and is ideal for fast prototypes and reproducible analysis. By combining structured automation and co-decision-manufacturing, this approach closes the gap between application accessibility and performance for machine learning
Introduction
Overview
The rapid evolution of AI and machine learning (ML) demands efficient, scalable, and interpretable automation. Traditional ML workflows are manual and inconsistent. Automated Machine Learning (AutoML) frameworks like TPOT and Auto-sklearn address these challenges but often lack transparency and adaptability.
CrewAI introduces a modular, agent-based framework that automates end-to-end ML workflows while ensuring reproducibility, interpretability, and user-friendliness for both technical and non-technical users.
Core Contributions
1. Framework Design & Objectives
Goal: Build agent-based automated ML pipelines using CrewAI.
Key Objectives:
Automate data loading, preprocessing, exploratory data analysis (EDA), model training, and reporting.
Use heuristic logic for dynamic target selection and adaptive preprocessing.
Integrate Pydantic for transparent data validation.
Reduce workflow execution time by up to 40%.
Produce interpretable outputs (models + reusable code).
Enable scalability and integration with advanced tools like deep learning, hyperparameter tuning, and real-time monitoring.
2. Architecture & System Design
Agents specialize in distinct tasks:
EDA Agent: Uses Pandas and Matplotlib/Seaborn for data analysis.
Model Selection Agent: Chooses algorithms based on data properties (e.g., RandomForest for classification).
Training Agent: Trains models and calculates performance metrics.
Tuning Agent: Uses GridSearchCV for hyperparameter optimization.
Reporting Agent: Compiles training code, metrics, and EDA insights into markdown reports.
Technologies Used:
Python, Scikit-Learn, LangChain, Pydantic, Streamlit, OpenAI API for NLP support.
Modular pipeline allows code generation via LangChain's PythonRepl and real-time report generation.
Results are auto-integrated into structured reports for clarity and traceability.
Performance Highlights:
80% reduction in manual workload.
Increased transparency and adaptability across diverse datasets.
Dynamic agent collaboration ensures robustness and flexibility.
User Experience & Interface
Streamlit UI:
Enables data upload, parameter selection, and real-time visualization.
Interactive widgets help adjust model settings and monitor pipeline progress.
Plans include cloud integration and dashboard enhancements.
Literature Insights
Compared with prior AutoML systems:
Auto-sklearn and TPOT optimize models but operate as black-boxes.
CrewAI emphasizes explainability and modular orchestration via agent collaboration.
Inspired by works on:
Human-AI collaboration
Interpretable ML
Modular and multi-agent systems
Model documentation (e.g., Model Cards)
Conclusion
The CrewAI-based ML pipeline offers a robust, scalable, and explainable AutoML solution. It fills the gap between automation and transparency through agent-based orchestration. The system democratizes ML by enabling both novice and expert users to build, understand, and iterate ML models efficiently.
References
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