project 05
ML Pipeline Agent
Helps data scientists transform exploratory machine learning code into structured, production-grade workflows. Uses a multi-agent architecture to analyze Python/Jupyter notebooks, identify ML components, and generate executable DAG pipelines.
Python · LLM · DAG · Jupyter · GitHub API · YAML
The problem
Research code is often messy, poorly structured, and difficult to deploy. The gap between ML experimentation and production pipelines creates significant delays and technical debt.
The approach
Built an AI-powered agent that analyzes ML repositories, identifies code components and their I/O attributes, generates DAG workflows with proper dependencies, and produces production-ready notebooks with configuration files.
The outcome
Significantly reduces the time to productionize research code while ensuring engineering best practices and reproducibility.
Key features
- Automatic file analysis to identify relevant ML code
- AI-powered component detection (data loading, preprocessing, training, evaluation)
- DAG generation in YAML format with proper dependencies
- Human-in-the-loop verification for quality control
- Production-ready notebook and config file generation
- Optional PR submission for generated pipelines