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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 principles of vector databases, outlines the engineering practices of Baidu Intelligent Cloud BES for large‑scale vector retrieval, discusses optimization techniques such as HNSW, IVF and filter integration, and presents real‑world AI use cases and future development directions.
The talk explores how the rise of large AI models is transforming cloud computing architecture, workloads, and services—shifting from traditional virtualization to heterogeneous compute, massive scaling, serverless infrastructures, and new networking designs that together enable agile AI‑native applications.
The article summarizes a Baidu Cloud Intelligence conference speech and whitepaper, explaining how AI foundations and large‑model infrastructure reshape applications, boost enterprise digital transformation, and drive regional economic development, offering a roadmap for high‑quality digital economy advancement.
This article summarizes Baidu’s senior manager Xu Peng’s presentation on the evolution from R&D‑focused to production‑ready autonomous driving toolchains, highlighting cloud simulation, data‑closed‑loop, AI‑driven labeling, compliance, efficiency, service, and cost challenges, and outlining Baidu’s integrated solutions for the automotive industry.
At the 2023 Baidu Cloud Intelligence Conference, Baidu AI Cloud outlined a comprehensive, four‑layer solution—spanning distributed cloud infrastructure, AI‑focused compute, data compliance, and end‑to‑end toolchains—to address the challenges of electric, intelligent vehicles, large‑model deployment, and regulatory compliance in autonomous driving.
In a keynote at Baidu Cloud Intelligence Conference, Baidu's VP Hou Zhenyu explains how the rise of large AI models is reshaping cloud infrastructure, spawning new architectures, services, and computing paradigms that converge mobile, deep learning, and cloud into an AI‑native era.
This article explains Baidu Intelligent Cloud’s tape‑library based cold‑data storage architecture, covering tape media basics, the Aries cloud storage system, its modular design, data flow, write and retrieval processes, and a real‑world deployment case that demonstrates cost‑effective petabyte‑scale archival.
In this interview, Baidu Smart Cloud’s big‑data lead Li Li explains how the team combined Elasticsearch with the open‑source Faiss engine, built custom columnar storage, and iteratively optimized their vector‑search service to support billions of high‑dimensional vectors for large‑model applications.
This article explains Baidu's CFS architecture for building a billion‑file‑scale distributed file system, covering basic file system concepts, POSIX limitations, metadata service modeling, performance metrics, evolution of metadata architectures, and CFS's lock‑free design that achieves high scalability, low latency, and balanced load in cloud storage.
