multimodal data

DataFunSummit

Sep 24, 2024 · Artificial Intelligence

Streaming Data Pipelines and Scaling Laws for Efficient Large‑Model Training

The article discusses the challenges of training ever‑larger AI models on internet‑scale data, critiques traditional batch ETL pipelines, and proposes a streaming data‑flow architecture with dynamic data selection and a shared‑memory/Alluxio middle layer to decouple data processing from model training, improving efficiency and scalability.

AI infrastructureLarge Modelsdata pipelines

0 likes · 20 min read

Streaming Data Pipelines and Scaling Laws for Efficient Large‑Model Training

AntTech

Sep 10, 2024 · Big Data

From DATA for AI to AI for DATA: Evolution of Ant Group’s Intelligent Data System

The talk reviews the rapid evolution of data technologies—from early database foundations and big‑data breakthroughs to the rise of generative AI—highlighting how Ant Group’s data platform is shifting from a cost‑efficiency focus to a value‑centric, multimodal, AI‑driven ecosystem.

Artificial IntelligenceBig DataData Engineering

0 likes · 17 min read

From DATA for AI to AI for DATA: Evolution of Ant Group’s Intelligent Data System

DataFunSummit

Jun 15, 2024 · Artificial Intelligence

Large‑Model‑Driven Data Governance: Technical Outlook and Research Highlights

This article reviews the rising importance of data quality for large models, explores data‑centric AI, large‑model pre‑training data engineering, and presents recent Fudan University research on using large models to improve data governance across multiple domains such as attribute normalization, geographic cleaning, compliance checking, and multimodal retrieval.

AIData EngineeringData Governance

0 likes · 19 min read

Large‑Model‑Driven Data Governance: Technical Outlook and Research Highlights

DataFunSummit

Aug 11, 2023 · Artificial Intelligence

Application of Knowledge Graphs in Risk Control at Wing Payment

This presentation details how Wing Payment leverages a large‑scale, multimodal knowledge graph and AI techniques—including computer vision, unsupervised and supervised learning, federated learning, and graph neural networks—to detect and mitigate fraud across payment, e‑commerce, and credit scenarios, while outlining system architecture, algorithmic approaches, case studies, and future research directions.

Artificial Intelligencefinancial servicesfraud detection

0 likes · 17 min read

Application of Knowledge Graphs in Risk Control at Wing Payment

58 Tech

Apr 16, 2021 · Artificial Intelligence

Graph Neural Network Based Anti‑Fraud Solution for Online Information Services

The article presents a comprehensive anti‑fraud framework that analyzes black‑market fraud characteristics, reviews conventional fraud‑mitigation methods, and proposes a multimodal graph‑neural‑network approach—leveraging device, behavior, and content similarity—to accurately identify fraudulent users on large‑scale internet platforms.

Graph Neural Networksanti-fraudfraud detection

0 likes · 18 min read

Graph Neural Network Based Anti‑Fraud Solution for Online Information Services