MLOps25 Workshop

As data-driven AI reshapes industries worldwide, deploying AI models into production environments poses unique engineering challenges that extend far beyond model development. The rise of large language models (LLMs) has further amplified these challenges, requiring innovative strategies to handle computational complexity, dynamic behavior, and real-world integration. Bridging the gap between experimental prototypes and robust, scalable AI systems demands specialized practices, tools, and methodologies.

Machine Learning Operations (MLOps)—and its recent extension for large language models (LLMOps)—has emerged as an essential framework for addressing these challenges, providing systematic methods to streamline the entire ML lifecycle. This workshop aims to bring together researchers and practitioners from academia and industry to explore operational challenges, cutting-edge advancements, practical implementations, and emerging trends in MLOps. Featuring interactive discussion sessions, peer-reviewed paper presentations, and invited keynotes, the event will encourage active engagement and collaboration.

We look forward to your participation and contributions! Please visit our call for papers for submission details.