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This article analyzes Baidu’s vertical‑search architecture team’s one‑stop development platform, detailing the background challenges, the FaaS and SaaS mechanisms introduced, design decisions, performance optimizations, dynamic form and DAG visualisation, and the resulting cost reductions and productivity gains.
The article presents the GEE (Graph Encoder Embedding) algorithm for business security anomaly detection, explains its label‑propagation foundation, evaluates it on ten‑million‑edge real data, identifies inefficiencies in the original implementation, and demonstrates that vectorized NumPy/Pandas optimizations reduce runtime from 55 seconds to about 4 seconds while preserving meaningful TSNE‑visualized embeddings.
AIAPI, Baidu’s AI‑native retrieval platform for large language models, tackles hallucination, slow domain updates, and output opacity by delivering authoritative, timely, full‑content data through a dual‑channel architecture that combines traditional search and RAG, employs reusable ranking, graph‑enhanced data layers, dynamic caching that cuts storage by 70 %, and QueryPlan‑based QoS, achieving markedly higher retrieval quality and a 34 % speed gain with Wenxin 4.0.
This article examines how an AI‑powered essay‑grading platform combines PaddleOCR and Baidu's Wenxin large model to automate scoring, generate personalized feedback, and reduce teachers' grading workload by over six times, while improving student learning outcomes across hundreds of Chinese schools.
The article introduces Jarvis, an AI-driven microservice governance platform that uses a multi-agent architecture and natural-language dialogue to automate full-process operations such as deployments, rate limiting, and circuit-breaker configuration, while leveraging large language model reasoning for root-cause diagnosis and a data-flywheel that continuously trains lightweight expert models.
PP‑ShiTuV2, a PaddleX pipeline that integrates subject detection, deep feature encoding, and vector retrieval, delivers 91 % recall@1 on AliProducts, surpasses earlier models by over 20 points, runs efficiently on GPU and CPU, and offers simple installation, quick‑start code, and full fine‑tuning support.
This article examines how Baidu and NVIDIA leveraged the open‑source RAFT library to build a GPU‑accelerated approximate nearest neighbor (ANN) retrieval system, detailing algorithm choices, offline indexing, online batch processing, performance results, and practical guidelines for deploying ANN on GPUs.
By replacing exponential row expansion with a data‑tagging strategy that encodes dimension combinations and aggregates at the user level, the authors cut Baidu Feed’s multi‑dimensional user‑count computation time from 49 to 14 minutes and shuffle size from 16 TB to 800 GB, enabling scalable analysis across dozens of dimensions for billions of daily users.
This article analyzes the evolution of data‑lake storage acceleration, compares traditional parallel file systems, object‑storage‑based solutions and modern cache‑enabled architectures, and explains how cloud‑native data lakes address scalability, cost, and performance challenges for AI and big‑data workloads.
Baidu’s Canghai Storage unifies metadata, hierarchical namespace, and data layers into a Meta‑Aware, three‑generation architecture that scales to trillions of metadata items and zettabyte‑scale data, using a distributed transactional KV store, single‑machine‑distributed namespace, and online erasure‑coding micro‑services to deliver high performance, low cost, and seamless scalability.
