Navigating Challenges and Technical Debt in Large Language Models Deployment

What is it about?

Large Language Models (LLMs) have become essential tools for advancing artificial intelligence and machine learning, revolutionizing natural language processing and understanding. However, deploying LLMs efficiently in production reveals a complex landscape of challenges and technical debts. This paper aims to highlight the unique forms of challenges and technical debts associated with LLM deployment. These challenges require tailored deployment approaches and customized, sophisticated engineering solutions not readily available in broad-use machine learning libraries or inference engines

Featured Image

Photo by Brannon Naito on Unsplash

Photo by Brannon Naito on Unsplash

Why is it important?

our work does not introduce new machine learning algorithmic research. Instead, it aims to broaden the understanding within the community regarding the technical challenges and the unique types of technical debt that arises when deploying LLMs