06-06, 15:30–17:00 (Europe/London), Hardwick Hub
Graph theory is a well-known concept for algorithms and can be used to orchestrate the building of multi-model pipelines. By translating tasks and dependencies into a Directed Acyclic Graph, we can orchestrate diverse AI models, including NLP, vision, and recommendation capabilities. This tutorial provides a step-by-step approach to designing graph-based AI model pipelines, focusing on clinical use cases from the field.
I will start by providing an introduction to orchestrating multiple models in a single workflow and explaining why conventional linear pipelines fail to meet complex tasks. Next, we’ll outline how graph theory addresses clinical tasks such as patient document workflow, starting from doctor notes, blood results analysis, and discharge letters. Finally, we will discuss how to scale the concept of multi-model integration in any field.
The tutorial will include live code demos, I will provide a GitHub repository with the tutorial code.
No previous knowledge expected
Ahmad is a data scientist with a Master from Illinois at Urbana-Champaign. He worked on a study to accelerate clinical tasks using language models and founded MedWrite AI company.
Ahmad is an active contributor to GitHub and has published open-source projects adopted by thousands of developers. He also writes articles about machine learning in various outlets to bridge the gap between research and practical applications.