International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Comprehensive Survey and Implementation of Distributed Automated Machine Learning (AutoML) Frameworks with MLOps Integration: The FlowML Studio |
|---|---|
| ABSTRACT | Machine Learning deployment often suffers from significant complexity and "hidden technical debt," hindering the transition from experimentation to production. This paper surveys the MLOps landscape, identifying critical limitations in existing tools like MLflow and Optuna regarding integration and usability. Addressing these gaps, we propose FlowML Studio, a privacy-first, distributed AutoML platform. By orchestrating FastAPI, React, and Celery, FlowML automates the entire lifecycle—from data ingestion to one-click model deployment. The system features a novel architecture supporting GPU acceleration and real-time observability, significantly reducing engineering overhead while ensuring data sovereignty. |
| AUTHOR | PROF. AMRITA SHIRODE, APURV SHARAD BHOSALE, SHUBHAM DNYANOBA ANDHALE, VEDANT NILESH DHUMAL, ARYAN PARVIJKHAN AWATI Department of Artificial Intelligence & Machine Learning, AISSMS Polytechnic, Pune, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402007 |
| pdf/7_A Comprehensive Survey and Implementation of Distributed Automated Machine Learning (AutoML) Frameworks with MLOps Integration The FlowML Studio.pdf | |
| KEYWORDS | |
| References | [1] D. Sculley et al., "Hidden Technical Debt in Machine Learning Systems," NeurIPS, 2015. [2] E. LeDell and S. Poirier, "H2O AutoML: Scalable Automatic Machine Learning," ICML Workshop, 2020. [3] C. Wang et al., "FLAML: A Fast and Lightweight AutoML Library," MLSys, 2021. [4] R. S. Olson and J. H. Moore, "TPOT: A Tree-Based Pipeline Optimization Tool," ICML Workshop, 2016. [5] P. Trirat et al., "AutoML-Agent: A Multi-Agent LLM Framework," arXiv:2410.02958, 2025. [6] FlowML Studio Documentation, GitHub Repository, 2026. |
