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Large-Scale Models

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DataFunSummit
DataFunSummit
May 8, 2024 · Artificial Intelligence

Kuaishou’s Practices for Large‑Scale Model Data Processing and Storage

This article shares Kuaishou’s real‑time, massive‑scale model data processing pipeline, covering model scenarios, recommendation workflow complexity, large‑scale data storage, streaming joins, feature computation, NVM‑based storage solutions, strong consistency mechanisms, and future outlook for AI recommendation systems.

AIKuaishouLarge-Scale Models
0 likes · 16 min read
Kuaishou’s Practices for Large‑Scale Model Data Processing and Storage
DataFunSummit
DataFunSummit
Apr 24, 2024 · Artificial Intelligence

Multimodal Content Understanding in Baidu Commercial Systems: The ViCAN Model and Its Applications

This article presents Baidu's exploration of multimodal content understanding for commercial advertising, detailing the ViCAN pre‑training model, its contrastive and mask‑language learning tasks, integration across recall, ranking and risk‑control pipelines, quantization with MMDict, and future AIGC‑driven generation, all backed by extensive experiments and Q&A.

AIAIGCLarge-Scale Models
0 likes · 27 min read
Multimodal Content Understanding in Baidu Commercial Systems: The ViCAN Model and Its Applications
Tencent Tech
Tencent Tech
Mar 26, 2024 · Artificial Intelligence

How Tencent Angel’s AI Platform Won the 2023 CIE Science & Tech Award

Tencent’s Angel machine‑learning platform, recognized with the 2023 China Institute of Electronics Science & Technology Award, showcases breakthrough distributed training, high‑efficiency caching, adaptive sampling, multimodal fusion, and graph‑model search technologies that dramatically improve large‑scale AI model performance and cost.

AILarge-Scale ModelsTencent
0 likes · 8 min read
How Tencent Angel’s AI Platform Won the 2023 CIE Science & Tech Award
iQIYI Technical Product Team
iQIYI Technical Product Team
Mar 1, 2024 · Artificial Intelligence

Advertising Data Characteristics and Sparse Large‑Model Practices at iQIYI

iQIYI’s ad ranking system replaces static, hash‑based embeddings with TFRA dynamic embeddings to efficiently handle massive sparse ID features, eliminates collisions and I/O bottlenecks, isolates memory during hot model swaps, enabling billion‑parameter models that boost revenue by 4.3 % while planning adaptive embedding sizes for future improvements.

AI recommendationDynamic EmbeddingLarge-Scale Models
0 likes · 10 min read
Advertising Data Characteristics and Sparse Large‑Model Practices at iQIYI
DataFunTalk
DataFunTalk
Feb 7, 2024 · Big Data

Kuaishou's Practices for Large‑Scale Model Data Processing, Real‑Time Feature Handling, and Storage

This article presents Kuaishou's end‑to‑end engineering solutions for handling massive, real‑time recommendation model data, covering scenario description, complex business pipelines, trillion‑parameter model storage, high‑throughput processing with Flink and NVM, and future directions for cloud‑native scalability.

KuaishouLarge-Scale ModelsNVM storage
0 likes · 15 min read
Kuaishou's Practices for Large‑Scale Model Data Processing, Real‑Time Feature Handling, and Storage
DataFunTalk
DataFunTalk
Apr 24, 2023 · Artificial Intelligence

Evolution of Large‑Scale Recommendation Models at Weibo: Technical Roadmap and Recent Advances

This article reviews the evolution of Weibo's large‑scale recommendation technology, covering the system's business scenarios, technical roadmap, recent large model iterations, multi‑task and multi‑scenario modeling, feature engineering, consistency between recall and ranking, and emerging techniques such as causal inference and graph methods.

Large-Scale ModelsRecommendation systemscausal inference
0 likes · 18 min read
Evolution of Large‑Scale Recommendation Models at Weibo: Technical Roadmap and Recent Advances
DataFunSummit
DataFunSummit
Apr 17, 2023 · Artificial Intelligence

Large‑Scale Table Pretraining Model SPACE‑T: Background, Architecture, and Applications

The article presents Alibaba DAMO Academy's large‑scale table pretraining model SPACE‑T, explaining the background and trends of TableQA and Text‑to‑SQL, detailing the model’s design and training data, showcasing its deployment on ModelScope and Alibaba Cloud, and outlining future directions and practical impact.

AILarge-Scale ModelsModelScope
0 likes · 11 min read
Large‑Scale Table Pretraining Model SPACE‑T: Background, Architecture, and Applications
Tencent Advertising Technology
Tencent Advertising Technology
Nov 17, 2022 · Artificial Intelligence

Scaling Huge Embedding Model Training with Cache-Enabled Distributed Framework (HET): VLDB 2022 Best Paper and Its Industrial Deployment

The award‑winning VLDB 2022 paper introduces HET, a cache‑enabled distributed framework that dramatically reduces communication overhead for sparse trillion‑parameter embedding models, and Tencent Ads has industrialized this technology to train 10 TB‑scale models with up to 7×24‑hour online deep learning.

Deep LearningLarge-Scale Modelscache
0 likes · 9 min read
Scaling Huge Embedding Model Training with Cache-Enabled Distributed Framework (HET): VLDB 2022 Best Paper and Its Industrial Deployment
Baidu Geek Talk
Baidu Geek Talk
Oct 31, 2022 · Artificial Intelligence

PaddleBox: A GPU‑Based Ultra‑Large‑Scale Sparse DNN Training Framework

PaddleBox is Baidu’s GPU‑based ultra‑large‑scale sparse DNN training framework that combines a three‑tier hierarchical parameter server (SSD, DRAM, HBM) with pipelined scheduling and multi‑machine multi‑GPU communication, delivering 5–40× cost‑performance gains over traditional CPU solutions and powering Baidu’s advertising services.

Deep LearningGPULarge-Scale Models
0 likes · 15 min read
PaddleBox: A GPU‑Based Ultra‑Large‑Scale Sparse DNN Training Framework
Sohu Tech Products
Sohu Tech Products
Sep 16, 2020 · Artificial Intelligence

Open-Domain Dialogue Systems: Current State, Challenges, and Future Directions

This article reviews the latest advances in open-domain dialogue systems, covering classification, end‑to‑end generation challenges, knowledge‑controlled generation, automated evaluation, large‑scale latent‑space models such as PLATO, and outlines future research directions for building more coherent and controllable conversational AI.

EvaluationLarge-Scale Modelsdialogue systems
0 likes · 14 min read
Open-Domain Dialogue Systems: Current State, Challenges, and Future Directions