The article argues that in the era of powerful large models, enterprises lack a unified, computable, and evolvable semantic layer—ontology—that acts as a semantic operating system, bridging business concepts, data, and AI to enable reliable, actionable intelligence.
The article highlights the rapid rise of reinforcement learning across major 2026 conferences, curates 181 RL papers from eight top venues, and provides detailed summaries of innovative works such as MSRL and MedVR, offering free access to the papers and code.
This article analyzes the rapid evolution of multimodal video generation models from separated visual‑audio pipelines to unified diffusion Transformers, detailing VAE compression, MoE scaling, cross‑modal alignment techniques, comprehensive evaluation metrics, real‑world applications, and the remaining technical challenges.
Facing siloed, tool‑fragmented, and low‑quality data pipelines, China Everbright Bank built an AI‑driven, end‑to‑end data integration platform that unifies heterogeneous databases, automates workflow checkpoints, and adds intelligent code quality checks, delivering faster, higher‑quality data services for the financial sector.
In a recent interview, OpenAI co‑founder Greg Brockman explains how a hard compute budget forces the company to prioritize a personal assistant and an AI work‑agent, consolidate products into a unified AI layer, and develop the next‑gen Spud model that could boost task coverage from 20% to 80%.
The 2026 AI4SE Industry Survey by China CAICT reveals that AI technologies—from large models to autonomous agents—are dramatically boosting efficiency across the software‑engineering lifecycle, raising maturity to core‑intelligence levels, expanding tool adoption, and driving a paradigm shift toward large‑scale, AI‑driven development.
The article analyzes the rapid evolution of China’s AI ecosystem, detailing the full open‑source release of GLM‑5.1 with GPT‑4o‑level performance, a 300% API usage spike after US restrictions, upcoming model roadmaps from major players, record‑high AI compute capacity, and unprecedented investment flows that together signal a strategic shift in the global AI race.
From early 2024 explorations to a 2026 research surge, this review explains how large‑model latent space replaces explicit token‑based processing, outlines its five analytical lenses—foundation, evolution, mechanism, ability, outlook—compares representational properties, details architectural and computational strategies, enumerates new capabilities, and discusses remaining challenges and future directions.
The new Video‑MME‑v2 benchmark reveals that despite saturated high scores on existing video‑understanding tests, the strongest commercial model (Gemini‑3‑Pro) reaches only 49.4 points versus a human expert’s 90.7, highlighting the benchmark’s layered ability system, group‑level non‑linear scoring, and the nuanced impact of "Thinking" features.
This article examines how AI large models can transform data warehouse development by automating modeling, improving data cleansing and quality auditing, and enabling intelligent operations, while also highlighting five common implementation pitfalls and practical best‑practice recommendations for enterprises seeking cost, efficiency, and quality gains.
A comprehensive industry roundup reveals that Chinese AI models have led global token usage for five weeks, new domestic app stores are generating million‑yuan developer revenue, US giants are restricting China IP access, and major funding, product launches, policy moves, and AI governance initiatives are reshaping the global AI landscape.
This roundup analyzes recent AI industry moves—including Alibaba’s sweeping organizational overhaul, a rare US AI‑giant alliance to curb Chinese competition, DeepSeek’s V4 launch, Zhipu’s GLM‑5.1 performance boost, Google’s 2027 AI production forecast, Anthropic’s Project Glasswing, new Chinese AI ethics rules, a massive Guangdong compute cluster, the open‑source SentiAvatar framework, soaring model‑stock prices, a token‑economy shift, and China’s first panoramic carbon‑accounting model—highlighting their strategic implications and concrete details.
The article dissects the four‑layer data architecture that transforms raw maritime charts into structured knowledge, enables large models to reason about navigation scenarios, and combines algorithmic route screening with LLM‑based refinement to deliver safe, compliant, and efficient autonomous ship routing.
This article analyzes how AI agents are reshaping software engineering, summarizing findings from 138 industry talks, highlighting integration challenges, architectural patterns, industry adoption forecasts, and practical recommendations for deploying robust, modular agent systems in production environments.
A rapid roundup shows how OpenAI’s GPT‑4o image generation overload, Musk’s $80 billion xAI‑X merger, Meta’s massive Llama 4 models, Apple’s Siri openness, and China’s soaring large‑model usage together illustrate shifting competitive dynamics and emerging market trends in the global AI industry.
This week’s roundup captures major AI breakthroughs and digital‑transformation moves, from OpenAI’s new flagship model and Google’s Gemini 3.2 Pro to China’s AI‑plus‑manufacturing pilots, domestic large‑model launches, and new regulatory guidelines shaping the industry worldwide.
The article analyzes how large‑language‑model development is moving from isolated text generation toward agent‑style, action‑oriented thinking, highlighting the technical challenges of reinforcement learning, mixed‑mode inference, environment design, and the industry’s strategic shift toward intelligent agents.
The article reveals that large‑model research has moved from sheer scale to deeper capabilities and multimodal integration, highlighting ten hot directions and summarizing 120 recent top‑conference papers—including Spec‑VLA, Mobile‑O, OccTENS, and latent‑CoT studies—while offering free access to the full collection.
China’s AI models logged 4.69 trillion tokens in a week, overtaking the US, while OpenAI plans to add 3,500 jobs to reach 8,000 staff, Musk announced a terawatt‑scale space compute project, MiniMax M2.5 topped global usage, Tencent reshuffled its AI Lab, and Xiaomi launched three new models, highlighting a shift from parameter‑centric competition to real‑world deployment and cost efficiency.
The article surveys recent AI breakthroughs and announcements, covering Nvidia's physical‑AI infrastructure, Tesla's AI6 chip, Huawei's partner conference and data platform, the MANSION framework for embodied intelligence, OpenAI's compute challenge, quantum cryptography advances, EverMind's MSA architecture, ZhiJi's LS8 pre‑sale, and Alibaba's cloud AI revenue target.
In the era of hundred‑thousand‑GPU clusters and trillion‑parameter models, conventional monitoring can no longer rely on simple alerts; it must become an observability system that quantifies training and inference performance, breaks data silos across data centers, servers, and networks, and provides business‑aware insights for AI infrastructure.
When interviewing for data engineering roles in AI large‑model teams, recruiters prioritize deep project experience, cutting‑edge tech stacks like Hudi or Paimon, extensive object and vector storage knowledge, and real‑time processing skills such as Flink.
SenseTime eliminates the intermediate encoder in multimodal AI models, allowing direct cross‑modal learning, which yields markedly higher performance at 2‑trillion‑parameter scale while reducing training cost, and may trigger a broader industry move toward simpler, more efficient architectures.
Engineers training large AI models often see noticeable FP16/BF16 result differences between GPUs and NPUs, and even between generations of the same chip, due to floating‑point representation limits, hardware design choices, software library implementations, compiler optimizations, and parallel execution nondeterminism.
AState is a general‑purpose state data management system for reinforcement‑learning tasks that tackles low IO efficiency, slow weight synchronization, and state‑recovery challenges, achieving sub‑10‑second weight sync for trillion‑parameter models through a three‑layer architecture, zero‑redundancy transfers, and hardware‑aware co‑design, with the code openly available on GitHub.
The Datafun Summit 2025 talk by JD’s algorithm engineer Tian Mingyang explains how generative AI is driving a paradigm shift in recommendation systems, detailing the limits of traditional models, the new dynamic cross‑domain inference chain technique, joint engineering‑algorithm optimizations, and the remaining challenges for future deployment.
Awex is a high‑performance Python framework that synchronizes training and inference weights for trillion‑parameter reinforcement‑learning models in seconds, using unified conversion, metadata management, and NCCL/RDMA transfer plans, dramatically reducing RL training latency and supporting diverse parallel strategies.
This issue of the tech weekly explores the latest breakthroughs where AI meets frontend development, highlighting the launch of the Vike framework, AI-driven code testing and debugging workflows, large-model innovations from major companies, open-source AI CRM and model projects, and practical insights for developers and engineers.
This article recounts how Zilliz leveraged the low‑code platform Dify to integrate large‑model AI, private data, and business logic, transforming an eight‑minute, manual sales‑lead workflow into a seconds‑level automated pipeline and illustrating a new human‑AI collaboration paradigm.
This article explains how to design, visualize, and automate testing for AI large‑model interior layout solutions, covering data importance, algorithm classification, validation pipelines, and platformization to accelerate model iteration and ensure reliable, design‑correct outputs.
This article examines the evolution from single‑frame video analysis to multimodal large models, detailing their architecture, optimization techniques, experimental validation on edge devices, and practical scenarios, while highlighting current limitations and future directions for AI‑driven video understanding.
The article explains how the rapid rise of large‑model AI has shifted the focus from models to data, outlines the concept and stages of AI‑specific data governance, identifies challenges such as low‑quality data, privacy leaks, bias, and proposes a comprehensive framework of principles, processes, and technologies to ensure high‑quality, secure, and ethical AI deployment.
At the GOPS Global Operations Conference in Shanghai, experts from finance, technology and energy sectors examined the challenges of observability, AIOps and intelligent agents, proposing metric standardization, digital‑twin fault simulation, and AI‑driven DevOps as key steps toward scalable, business‑value‑focused intelligent operations.
This guide explains why Docker is ideal for deploying large AI models and walks you through every step—from preparing the model and API code, building and running Docker images, to testing, optimizing, and finally deploying the containerized service in production environments.
At the 2025 Vivo Developer Conference, Baidu Security earned the Best Security Technology Partner award for its edge‑focused large‑model security solution, which tackles multi‑layered threats on devices through comprehensive content protection, tailored edge defenses, advanced attack detection, and a rigorous evaluation framework.
The article summarizes a Cloudwise conference talk that explains how AI data differs from traditional big data in organization, cost, and comprehension, and describes why Tongyi Lab built a unified large‑model data platform on MaxCompute, detailing its architecture and processing pipelines.
The article examines the rapid flood of new large‑language models and AI tools, explains why many professionals feel "AI anxiety," presents a data‑driven comparison of model hallucination rates, and offers a step‑by‑step personal framework for learning, building custom agents, and maintaining independent, rational thinking in the AI era.
This article examines how AI‑generated content (AIGC) is reshaping e‑commerce video creation by addressing the limitations of rule‑based pipelines, introducing a MultiAgent collaborative system, enriching material sources, leveraging large‑model editing, and outlining future directions for fully generative video production.
This article reviews Kuaishou's two‑year exploration of multimodal large‑model techniques for advertising, outlining challenges in content‑domain ad estimation, the COPE unified product representation framework, and the LEARN LLM knowledge‑transfer approach that together improve ad system performance.
This article reviews Kuaishou's two‑year exploration of large‑model techniques in advertising, outlining challenges in content‑domain ad estimation, introducing the COPE unified content representation framework and the LEARN LLM knowledge‑transfer approach, and showing how these innovations delivered tangible business gains.
This article reviews Kuaishou's two‑year exploration of multimodal large‑model techniques for advertising, detailing challenges of fragmented user behavior, the COPE unified product representation framework, and the LEARN LLM knowledge‑transfer approach that together delivered measurable business gains.
This article reviews Kuaishou’s two‑year exploration of large‑model techniques in advertising, detailing the content‑domain estimation challenges, how multimodal and LLM approaches improve full‑domain behavior utilization and external knowledge integration, and introducing the COPE product‑content representation framework and the LEARN LLM knowledge‑transfer system.
This article outlines Kuaishou's two‑year exploration of large‑model techniques in advertising, detailing challenges of sparse cross‑domain data, the COPE unified product representation framework, and the LEARN LLM knowledge‑transfer approach that together improve ad system effectiveness.
Over the past two years, Kuaishou has leveraged multimodal large‑model techniques to overcome sparse advertising data, integrating full‑domain user behavior and external knowledge via the COPE unified product representation framework and the LEARN LLM knowledge‑transfer system, achieving measurable business gains.
The TLP platform offers comprehensive support for labeling text, image, audio, and video data, detailing the importance of high‑quality annotations for large AI models, task creation, workflow steps, review processes, and an automatic annotation feature that can boost efficiency by over 60 percent.
In his 2025 Cloud Conference keynote, Alibaba Cloud CEO Wu Yongming outlines a three‑stage roadmap from AGI to superintelligent AI, describes large models as the next operating system, and explains the massive infrastructure and open‑source strategy needed to power the future AI era.
The DataFun 2025 GenAI Summit brings together top AI researchers and industry leaders to explore the latest breakthroughs in large‑model technology, generative recommendation, AI‑driven finance, video understanding, legal AI, and marketing, featuring five heavyweight forums, live demos, and a QR‑code registration for free access.
The article examines how large‑model AI is reshaping R&D by advancing intelligent development tools—from early code‑completion assistants like GitHub Copilot to expansive code‑generation platforms—highlighting their evolution, enterprise adoption, practical developer experiences, and future workflow transformations.
This article presents a curated list of cutting‑edge topics covering AI‑powered data governance, large‑model applications, intelligent operations, and advanced analytics, offering readers a concise overview of emerging practices and case studies from industry leaders.
The DataFun Summit 2025 showcases leading experts presenting the latest breakthroughs in generative AI, large‑model research, and industry‑specific applications—from financial analysis and e‑commerce recommendation to video understanding and intelligent agents—offering attendees actionable insights to accelerate AI adoption across sectors.
The DataFun GenAI Summit 2025 brings together leading experts from Alibaba, Tencent, Ant Financial, and other tech giants to showcase the latest breakthroughs in large-model research, generative AI, multimodal understanding, and real-world deployments across finance, e-commerce, media, and enterprise services.
This Tencent Research Institute and KPMG report examines large-model AI deployment in the financial sector, stressing that success hinges on ROI-driven strategies rather than a technology-for-technology's-sake race, and offers practical, forward-looking guidance on model building, scenario application, and digital transformation for financial institutions.
This presentation explores AI-era recommendation strategies, defines strategy product roles and ability models, outlines the three generations of product managers, discusses AI-driven trends, workflow simplifications, 2024 observations, and offers practical guidance for career growth in product management.
The article outlines five key ways AI large models can revamp enterprise data warehouses—automated data governance and cleaning, natural‑language query interfaces, real‑time predictive analytics, multimodal data integration with knowledge graphs, and security‑compliant automated operations—while also discussing supporting technologies, toolchains, and future trends toward industry‑specific models.
The 2025 Inclusion Bund conference revealed a rapidly evolving open‑source large‑model ecosystem, highlighting 114 top projects across 22 fields, a 35% update rate, global developer distribution, community initiatives like Modak and Silicon Flow, and forward‑looking discussions on Vibe Coding, AI Agents, and the future of AI development.
This article presents a curated catalog of cutting‑edge topics covering financial large‑model data governance, proactive metadata systems, data cleaning and compliance technologies, AI‑driven intelligent operations, and generative data analysis solutions, inviting readers to explore the latest AI innovations.
This article presents a curated catalog of cutting‑edge topics covering AI‑powered data governance, large‑model applications, data cleaning, compliance, lakehouse integration, intelligent operations, and generative analytics, inviting readers to explore the latest innovations and download the full e‑book via QR code.
This article introduces six open‑source projects—including a visual guide for large‑model reinforcement learning, Alibaba's WebAgent suite, a 12‑factor AI‑agent handbook, Google’s MCP database toolbox, the Graphiti knowledge‑graph engine, and a Rust‑based distributed object store—each with key features and GitHub links.
The Inclusion·外滩大会 forum explored how diminishing returns from massive models demand a shift toward cognitive reasoning, autonomous evolution, multi‑agent coordination, reinforcement learning, high‑quality data, and MoE diffusion models to bridge digital AI with the physical world.
This week’s tech roundup highlights ByteDance’s OmniHuman‑1.5 virtual humans with logical reasoning, Apple’s chief robotics researcher joining Meta, Meituan’s LongCat‑Flash model that schedules compute like delivery, the launch of 6G standardization work, Anthropic’s ban on Chinese‑controlled users, OpenAI’s AI‑leadership whitepaper, and other notable AI and hardware advances.
This article presents a comprehensive catalog of cutting‑edge AI and large‑model topics, covering financial data governance, proactive metadata systems, data cleaning compliance, lake‑warehouse integration, intelligent operations, generative analytics, and QR‑code access to the full e‑book.
Recent moves by OpenAI, which acquired product‑testing startup Statsig for $11 billion, and Anthropic, which secured $13 billion in Series F financing, illustrate a broader shift in the large‑model race from pure model‑centric competition to a focus on application, ecosystem building, commercial viability, and cost control.
The article explains how to fine‑tune large multimodal models by focusing on the projection layer, optionally using LORA for language‑model adaptation, and highlights data alignment, common applications, and the added difficulty of modality alignment for interview preparation.
The article highlights a new 2024‑2025 Chinese AI large‑model market research report, noting a 2024 market size of approximately 294.16 billion CNY and a projected exceedance of 700 billion CNY by 2026, while also revealing high user engagement rates, with over half using models multiple times weekly.
This resource presents a curated collection of cutting‑edge case studies and technical insights on financial large‑model data governance, proactive metadata‑based intelligent governance, data cleaning and compliance, AI‑powered operations, BI+AI innovations, and generative data analysis across leading industry players.
This article presents a curated collection of cutting‑edge case studies and research on AI‑driven data governance, large‑model data cleaning, intelligent operations, and innovative analytics from industry leaders such as JD, Alibaba Cloud, Ping An, Didi, and Kuaishou.
This article presents a curated list of advanced topics covering AI-driven data governance, large‑model applications, intelligent operations, and BI+AI practices from industry leaders such as JD, Alibaba Cloud, Ping An, Didi, and Kuaishou, along with a QR code to download the full e‑book.
This article presents a curated list of cutting‑edge resources covering AI‑powered data governance, large‑model driven data cleaning, intelligent operations, and generative analytics techniques from industry leaders such as JD.com, Alibaba Cloud, Ping An Life, Didi, and Kuaishou.
The article details JD.com’s AI‑driven supply‑chain innovations, from dynamic demand forecasting and risk‑aware decision loops to a groundbreaking billion‑parameter time‑series model that boosts inventory efficiency, reduces costs, and reshapes global logistics ecosystems.
The article details JD.com’s AI‑driven supply‑chain innovations, including a billion‑scale pure time‑series model, advanced demand forecasting, intelligent inventory selection, and end‑to‑end allocation algorithms that dramatically improve efficiency, cost, and global supply‑chain transformation.
The DA Data Intelligence Conference in Shenzhen showcased SF Tech’s breakthroughs in large‑model AI, revealing how their proprietary multimodal models, RAG innovations, and agent platforms dramatically improve logistics decision‑making, resource scheduling, and customer service across multiple industries.
This report examines the rise of AI agents as the primary form of large‑model applications, detailing their development overview, core technologies, industry use cases, current challenges, and strategic recommendations to accelerate the integration of artificial intelligence with the real economy.
Prof. Dou Dejing shares his journey into Fudan University's Data Intelligence Lab, outlines the history and synergy of big data and AI, reviews generative AI breakthroughs, evaluates large‑model strengths and weaknesses, and explores their expanding industrial applications and market potential.
This resource compiles a curated list of cutting‑edge studies and frameworks that combine multimodal GraphRAG, knowledge‑graph techniques, and large‑model AI across sectors such as finance, healthcare, and manufacturing, guiding readers through emerging paradigms and future directions.
SF Technology explains how it tackles AI hallucination, ensures responsibility and safety, and boosts operational efficiency in logistics by combining rule‑based layers, private deployments, low‑code platforms, and domain‑specific model fine‑tuning to create industrial‑grade AI agents.
SF Technology explains how it builds responsible, safe, and professional AI agents for logistics by combining rule‑based layers, a logistics knowledge base, private deployment, and low‑code platforms, while tackling hallucination, inference performance, and tool‑calling challenges to achieve industrial‑grade decision loops.
PAI‑ChatLearn is a flexible, easy‑to‑use, high‑efficiency reinforcement‑learning framework on Alibaba Cloud’s AI platform that addresses usability and performance challenges of post‑training large models through features like Ray‑based scheduling, dynamic batchsize, sequence packing, MoE acceleration, and provides step‑by‑step guidance for deploying RL tasks such as Qwen‑3 on PAI‑DLC.
This article presents a detailed collection of AI large‑model architecture diagrams covering general frameworks, domain‑specific solutions such as RAG knowledge bases, agriculture, retail, IoT, compliance, and CRM integration, while also noting copyright considerations for the visual content.
China’s first group standard, the “AI Large Model Private Deployment Technical Implementation and Evaluation Guide,” has been launched to address the technical, security, and cost challenges of privately deploying large AI models, offering a unified framework for model selection, deployment, optimization, and multi‑party collaboration.
In an exclusive interview, SF Express’s AI leader Liu Ziheng explains how the company builds responsible, safe, and collaborative logistics AI agents, detailing anti‑hallucination mechanisms, private‑cloud deployment, model fine‑tuning, knowledge‑base integration, low‑code platforms, and the technical hurdles to scaling intelligent agents industry‑wide.
Join the DataFun Summit 2025 on July 12 to hear Tencent FinTech senior researcher Gong Dihong discuss how redesigning the Verl training system, integrating Megatron and Sglang, and applying new synchronization and offloading techniques dramatically speeds up large‑model reinforcement‑learning training.
In a QECon 2025 talk, Ant Group’s AI team detailed a comprehensive multimodal evaluation framework that leverages large‑model metrics, custom pipelines, and benchmark datasets to assess image generation, speech recognition, and video quality, while also contributing to industry standards and academic research.
On June 16, the China Academy of Information and Communications Technology released the first edition of the Intelligent Agent Industry Map, highlighting over 200 companies, detailing foundational, generic, and specialized layers, and showcasing 360’s Nano AI super‑search agent and MCP toolbox as key examples of the emerging AI‑agent ecosystem.
Join Baidu senior algorithm engineer Ji Zhi at the DataFun Summit 2025 to explore how generative AI transforms ad recommendation recall, covering item representation, evolving solution architectures, long‑sequence challenges, and practical insights for building efficient large‑model recommendation systems.
This article examines how AI large models reshape enterprise data warehouses through intelligent data governance, natural‑language query conversion, real‑time predictive analytics, multimodal knowledge integration, and automated security compliance, while outlining supporting technologies, toolchains, and future trends.
OneRec, an end-to-end generative recommendation system from Kuaishou, uses an encoder-decoder architecture, reward-based preference alignment, and reinforcement learning to dramatically improve video recommendation efficiency, boosting user engagement and reducing operational costs while achieving scaling-law performance comparable to large language models.
JD‑Tech leveraged large‑model innovations and a novel AnchorDP3 3D diffusion policy to win both stages of the CVPR 2025 dual‑arm manipulation competition, showcasing breakthroughs in synthetic data generation, multimodal perception, and precise trajectory control for embodied AI robots.
The article details JD Retail’s collaboration with Tsinghua University to build the xLLM edge‑cloud unified large‑model inference framework, addressing e‑commerce AI challenges such as diverse inputs, task scheduling, model compression, and cost, while outlining future research directions and performance gains.
The GIAC 12th Global Internet Architecture Conference in Shenzhen gathered experts from Kuaishou, ByteDance, Tencent, iFlytek and Bilibili to discuss large‑model industrialization, generative AI, AI safety, cloud‑native and multimodal technologies, presenting 59 cutting‑edge architectural cases and insightful talks from leading innovators.
This article presents a collection of architecture diagrams that give a panoramic overview of general AI large‑model frameworks and showcase specialized designs for RAG knowledge bases, agriculture, e‑commerce, IoT, compliance, agent platforms, and CRM integration.
This article details Alibaba Cloud AI Search’s development journey, covering its dual product lines, the evolution of Agentic RAG technology, multi‑agent architectures, vector retrieval breakthroughs, GPU‑accelerated indexing, NL2SQL capabilities, deployment models, and future directions for AI‑driven search solutions.
This whitepaper examines the accelerating growth of enterprise data, the limitations of traditional rule‑based governance, and how multimodal AI large models—combined with privacy‑preserving techniques—can create a four‑layer, six‑domain architecture that automates metadata management, quality control, and compliance, delivering measurable efficiency gains across finance, manufacturing, and retail sectors.
This article presents a panoramic view of AI large‑model technologies, showcasing a series of architecture diagrams that illustrate general model frameworks, RAG knowledge‑base structures, agricultural and retail applications, IoT integration, compliance and risk‑management setups, agent platforms, and CRM‑enhanced solutions.
The article explains how rapid AI advances and the AIAgent architecture are reshaping software development, outlines four key technical drivers—embedding, Transformer scaling laws, scenario Moore's law, and LLM OS—and discusses the security, professionalism, and responsibility challenges enterprises face when deploying AI‑native applications.
This article shares a detailed, first‑hand analysis of the technical challenges, framework choices, memory management, weight conversion, precision alignment, and efficiency optimizations encountered while building reinforcement‑learning pipelines for a 671‑billion‑parameter model using the verl ecosystem.
This article explains how the rapid development of large language models and knowledge graph technologies creates new opportunities for enterprise knowledge management, outlines the challenges of massive unstructured data, describes the architecture and core data flow of a corporate knowledge brain, and showcases key technologies and real‑world applications.
The article explains how the Dolphin AI platform tackles large‑model deployment challenges by replacing standard Kubernetes Deployments with LeaderWorkerSet, detailing its architecture, features, installation steps, example configurations, testing, scaling, rolling updates, fault recovery, and future roadmap for AI workloads.
In his OceanBase developer conference keynote, Ant Group CTO He Zhengyu analyzes how the explosion of data fuels AGI progress, outlines four key data challenges—cost, scarcity, multimodality, and quality assessment—and argues that overcoming them will turn data companies into AI leaders.
The JD Exploration Institute paper introduces Omniforce, a human‑centered, cloud‑edge collaborative AutoML system that uses model distillation, dynamic data governance, Bayesian‑optimized training, and edge deployment to cut large‑model training costs by 70% and improve inference speed by 30%, powering the JoyBuild platform for broader AI adoption.
The article explains how large‑scale AI model training and inference require massive data handling, describes the RapidFS storage acceleration cluster deployed on a 30,000‑card Kunlun chip system with hundreds of domestic CPU servers, and presents performance tests showing linear throughput scaling up to over 1 TiB/s, demonstrating the impact of high‑performance storage on compute efficiency.
