As large language models (LLMs) gain traction, companies encounter challenges in deploying them effectively. This session focuses on using Airflow to manage LLM batch pipelines, addressing rate limits and optimizing asynchronous batch APIs. We will discuss strategies for managing cloud provider rate limits efficiently to ensure uninterrupted, cost-effective LLM operations. This includes queuing and job prioritization techniques to optimize throughput. Additionally, we’ll explore asynchronous batch processing for tasks such as Retrieval Augmented Generation (RAG) and vector embedding, which enhance processing efficiency and reduce latency. The session features a hands-on demonstration on AWS’s managed Airflow service, providing practical insights into configuring and scaling LLM workflows in the cloud.
