GenAI_ModelHub: Generative AI Powered Data Science Automation Platform

Abstract

The \"GenAI-ModelHub\" project is intended to dramatically streamline and automate the major parts of the data science workflows, offering highly powerful tools that accelerate complex tasks using the best AI technologies available. It will primarily target issues related to querying, model building, and optimization while integrating the SQL and Pandas data manipulation processes. It simplifies hyperparameter tuning and optimizes deep learning architectures for both novice and expert users by automating the creation of baseline models. At the heart of GenAI-ModelHub\'s functionality are Large Language Models1 (LLMs) and Retrieval-Augmented Generation2 (RAG). These technologies power an intelligent, interactive chatbot that can assist users in real time, automate data preprocessing steps, and generate code for model training—all tailored to the specific needs and data of the user. This approach helps users quickly move from data querying to model training, saving time and reducing the technical expertise required for complex configurations.

Introduction

In the era of data abundance, managing and analyzing large, complex datasets remains a significant challenge, requiring diverse skills in tools like SQL, Pandas, and machine learning frameworks. This proposed integrated platform addresses these challenges through four key modules, aiming to streamline and automate data science workflows:

1. SQL and Pandas Query Bridging

• Bridges the gap between SQL and Pandas by translating queries between the two.

• Powered by LLMs and RAG, enabling real-time chatbot assistance for query writing and interpretation.

• Useful for users proficient in one language but not the other.

2. Automatic Baseline Model Generation

• Automatically processes raw data to generate baseline models.

• Provides accuracy metrics, visual insights, statistical summaries, and R code for reproducibility.

• Encourages better starting points for machine learning model development.

3. Hyperparameter Tuning

• Offers an interactive UI to explore and tune model hyperparameters.

• Helps users understand the bias-variance trade-off with clear explanations and chatbot guidance.

• Recommends optimal parameters based on dataset characteristics, improving model accuracy with fewer iterations.

4. Deep Learning Optimization

• Automates the selection of neural network parameters (layers, strides, filters).

• Uses dataset characteristics to recommend optimal CNN architectures.

• Saves time by reducing trial-and-error in deep learning model development.

Technological Foundation

• Leverages generative AI (e.g., LLMs), transformers, text-to-code translation, and optimization algorithms.

• Supports users at all expertise levels with intuitive interfaces and automated backend logic.

Literature Support

• Reviews advancements in:

  • Text-to-SQL generation using T5 transformers and SQL-PaLM

  • Hyperparameter optimization methods (Bayesian, evolutionary algorithms)

  • Code generation using transformers

  • Optimization methods like L-BFGS vs. SGD

  • Importance of quality control in generated code

Expected Outcomes

• Seamless transition between SQL and Pandas queries.

• Efficient generation of baseline models with reproducible analysis.

• Enhanced hyperparameter tuning for better-performing models.

• More accurate and less complex deep learning architectures.

• Accessibility for users with varying skill levels through chatbot support and clear guidance.

Conclusion

This project is an example of how automation in machine learning transforms the availability and efficiency of more advanced tools to users at any level of expertise. The integration of natural language query translation to SQL and Pandas, combined with real-time support through a chatbot, helps bridge technical knowledge gaps and makes it more usable. The automatic generation of the baseline model requires shorter time periods to establish a solid foundation for iterative improvement while the interactive module for hyperparameter tuning powers the user to optimize models easily and understand bias-variance trade-offs. Further, deep learning optimization shrinks the cycle of model building with effort by guiding users towards the optimal configuration thereby expediting easy selection since it diminishes trial and error, especially on complicated data. Together, these features contribute not only to more intuitive workflow for data science but also pave the way for more broad adoption of AutoML. These show how automation may democratize the capability to apply machine learning and simplify the process for industry, research, and education-again, opening interesting roads for further development. Future versions could take these features further by adding multi-language support and API integrations to extend the impact of the tool across various domains.

References

[1] Application of Noise Filter Mechanism for T5-Based Text-to-SQL Generation by M.R. Aadhil Rushdy1, UthayasankerThayasivam2.1DepartmentofComputer Science and Engineering University of MoratuwaKatubedda, Sri Lanka, 2Department of Computer Science and Engineering University of MoratuwaKatubedda, Sri Lanka. [2] ASurveyonHyperparameter OptimizationofMachine Learning Models by Monica (Department of CSE&IT Jaypee Institute of Information Technology Noida, India monica.batra88@gmail.com), Parul Agrawal (Department of CSE&IT Jaypee Institute of Information Technology Noida, India parul.agarwal@ jiit.ac.in). [3] Textual Query Translation into Python Source Code using Transformers by Rutuja Nikum1, Vaishnavi Shinde2, Vijay Khadse3. 1,2,3Computer Engineering College of Engineering Pune, India. [4] An Empirical Study of Code Smells in Transformer- based Code Generation Techniques by Mohammed Latif Siddiq1 , Shafayat H. Majumder2 , Maisha R. Mim2 , Sourov Jajodia2 , Joanna C. S. Santos1, 1Department of Computer Science and Engineering, University of Notre Dame, USA, 2Department of Computer Science, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. [5] A Combinatorial Approach to Hyperparameter Optimization by Krishna Khadka1, Jaganmohan Chandrasekaran2, Yu Lei3, Raghu N. Kacker4, D. Richard Kuhn5. 1,3University of Texas at Arlington, Arlington,TX,USA.2NationalSecurityInstitute,Virginia Tech Arlington, VA, USA. 4,5Information Technology Laboratory, National Institute ofStandards and Technology. [6] Hyperparameter Optimization to Improve Bug Prediction Accuracy by Haidar Osman, Mohammad Ghafari, and Oscar Nierstrasz Software Composition Group, University of Bern, Bern, Switzerland. [7] Conversion of Natural Language Queryto SQL Query by Abhilasha Kate1, Satish Kamble2, Aishwarya Bodkhe3, Mrunal Joshi4. 1,2,3,4Dept. of IT Engineering PVG’s COET, Pune, India. [8] OnOptimizationMethodsforDeepLearningby Quoc [9] Le1, JiquanNgiam, Adam Coates, Abhik Lahiri, Bobby Prochnow, Andrew Y. Ng. Computer Science Department, Stanford University, Stanford, CA94305, USA

Copyright

Copyright © 2025 Mr. S. D. Kale, Ajinkya Temgire, Digvijay Mane, Arpita Pol, Priyanka Patil. 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.