This project focuses on the development of a low-code platform for designing and generating AI pipelines. The objective is to improve the efficiency and accessibility of AI/ML pipeline development by providing a framework that simplifies the process for researchers and practitioners, particularly those without extensive programming or AI expertise.
Researchers in various scientific domains rely on AI and Machine Learning (ML) for data analysis and decision-making. However, developing AI/ML pipelines tailored to specific domain requirements remains a challenge. This project aims to:
- Analyze and model AI pipelines from different research fields.
- Identify and formalize common and variant features across AI pipelines.
- Develop a low-code solution to automate the generation of AI pipelines.
- Validate the solution in collaboration with researchers from various disciplines.
- Low-Code Interface: A user-friendly environment to create AI pipelines with minimal coding.
- AI Pipeline Modeling: Standardized representation of AI workflows.
- Automation: Auto-generation of AI/ML workflows based on user-defined intentions.
- Domain-Specific Adaptability: Customizable for various research fields.
For inquiries, please contact:
📩 Jessie Galasso-Carbonnel
📧 jessie.galasso-carbonnel@mcgill.ca
📍 Room 535, McConnell Engineering Building, 3480 University, Montreal, QC H3A 0E9, Canada
We welcome contributions! Follow these steps:
- Fork the repository on GitHub.
- Clone your fork locally:
git clone https://github.com/your-username/low-code-ai-pipeline.git