Enterprise LLMOps & Continuous Evaluation Pipelines

Scaling artificial intelligence across an enterprise requires robust automation, reliable delivery, and rigorous monitoring to maintain system integrity. This lecture explores the foundations of Enterprise LLMOps & Continuous Evaluation Pipelines, part of the Enterprise AI Scaling & Distributed LLMOps course. Learn how organizations implement automated continuous integration and delivery frameworks specifically designed for managing prompts and machine learning models in production. As enterprise applications rely increasingly on large language models, maintaining output quality and preventing degradation become critical challenges. This session covers the mechanics of continuous evaluation, focusing on identifying evaluation harms, detecting prompt drift, and implementing proactive regression testing. You will also discover how to generate synthetic test data at scale and establish reliable LLM benchmarking pipelines to measure model performance over time. By the end of this lecture, you will understand how to architect automated CI/CD pipelines that safeguard your AI deployments against accuracy drops and unexpected behaviors. Key topics covered: Automated CI/CD for Prompts & Models Continuous Evaluation Harms & Drift Synthetic Test Data Generation Regression Testing LLM Benchmarking Pipelines