This article reviews the development of MLOps, introduces the emerging LMOps framework for large‑model engineering, outlines key architectural components, discusses current challenges and industry trends, and presents future directions and standardization efforts in AI operations.
This article reviews the evolution from MLOps to LMOps, outlines the core concepts, challenges, and key technologies such as large‑model inference optimization, prompt engineering, and context‑length extension, and offers a forward‑looking perspective on the future of AI operations.
This article reviews the evolution from MLOps to LMOps, outlines the fundamentals, challenges, and key technologies of large‑model operations—including inference optimization, prompt engineering, and context‑length extension—and presents Baidu AI Cloud's platform solutions and future outlook.
This article explains the evolution from traditional machine learning to the current large‑model era, introduces LMOps concepts and key technologies, compares them with MLOps, and showcases Baidu Cloud's Qianfan platform as a practical solution for building, deploying, and managing large language models in industry.
