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Lao Guo's Learning Space
Lao Guo's Learning Space
Apr 29, 2026 · Big Data

Designing a Full-Stack Credit Data System: From Ingestion to Real-Time Decision

The article dissects a credit data system architecture, detailing six logical layers—from multi-source data collection and feature engineering (including graph features and feature stores) to model training, real‑time stream processing, decision engine integration, and privacy‑preserving computation—while explaining the trade‑offs, tools, and performance targets needed for accurate, low‑latency risk assessment.

Credit ScoringFeature StoreFlink
0 likes · 16 min read
Designing a Full-Stack Credit Data System: From Ingestion to Real-Time Decision
Bitu Technology
Bitu Technology
Mar 21, 2025 · Backend Development

Optimizing Redis Latency for an Online Feature Store: A Batch Query Case Study

This article describes how Tubi improved the latency of its Redis‑backed online feature store for machine‑learning inference by analyzing query patterns, measuring client‑side bottlenecks, and applying optimizations such as binary Avro encoding, MGET usage, virtual partitioning, and parallel deserialization to meet a sub‑10 ms SLA.

Feature StoreLatencyMLOps
0 likes · 9 min read
Optimizing Redis Latency for an Online Feature Store: A Batch Query Case Study
DataFunSummit
DataFunSummit
Feb 14, 2025 · Artificial Intelligence

Building Large‑Scale Recommendation Systems with Big Data and Large Language Models on Alibaba Cloud AI Platform

This presentation details how Alibaba Cloud's AI platform integrates big‑data pipelines, feature‑store services, and large language model capabilities to construct high‑performance search‑recommendation architectures, covering system design, training and inference optimizations, LLM‑driven use cases, and open‑source RAG tooling.

AI PlatformBig DataDistributed Training
0 likes · 17 min read
Building Large‑Scale Recommendation Systems with Big Data and Large Language Models on Alibaba Cloud AI Platform
DataFunSummit
DataFunSummit
Oct 11, 2024 · Artificial Intelligence

Feature Production and Component Modeling in the Intelligent Era: From Feature Generation to Modular Modeling

This article introduces a cloud‑based feature production platform that simplifies feature engineering for recommendation, risk control and machine learning, explains its component‑based modeling framework, and answers common questions about deployment, performance, and customization, highlighting cross‑platform compatibility and optimization techniques.

Big DataFeature StoreModular Modeling
0 likes · 19 min read
Feature Production and Component Modeling in the Intelligent Era: From Feature Generation to Modular Modeling
NewBeeNLP
NewBeeNLP
Aug 5, 2024 · Industry Insights

How Alibaba Cloud Scales Search Recommendations with Big Data, AI, and LLMs

This article details Alibaba Cloud's end‑to‑end architecture for search and advertising recommendation, covering the data platform, AI services, feature‑store design, training and inference optimizations, and the integration of large language models for new recommendation scenarios.

AI PlatformAlibaba CloudBig Data
0 likes · 17 min read
How Alibaba Cloud Scales Search Recommendations with Big Data, AI, and LLMs
DataFunTalk
DataFunTalk
Aug 2, 2024 · Artificial Intelligence

From Big Data to Large Models: Alibaba Cloud AI Platform Architecture and Practices for Search Recommendation

This presentation details Alibaba Cloud's AI platform, covering the end‑to‑end pipeline from big‑data processing and feature engineering to large‑model training, inference optimization, recommendation system architecture, and RAG applications, highlighting practical engineering solutions and performance gains.

AI PlatformBig DataFeature Store
0 likes · 18 min read
From Big Data to Large Models: Alibaba Cloud AI Platform Architecture and Practices for Search Recommendation
DataFunSummit
DataFunSummit
Jun 17, 2024 · Artificial Intelligence

Strategies for Reducing Cost and Improving Efficiency in Recommendation Systems with Alibaba Cloud PAI‑Rec

This article discusses how Alibaba Cloud’s AI platform PAI‑Rec reduces recommendation system costs and boosts efficiency by optimizing training resources, leveraging FeatureStore, EasyRec and TorchEasyRec frameworks, detailing workflow stages, feature consistency, GPU acceleration, componentized model configuration, and practical deployment timelines.

AI PlatformFeature StoreGPU Acceleration
0 likes · 14 min read
Strategies for Reducing Cost and Improving Efficiency in Recommendation Systems with Alibaba Cloud PAI‑Rec
DataFunTalk
DataFunTalk
May 11, 2023 · Big Data

Scaling ByteDance Feature Store to EB‑Level with Apache Iceberg: Architecture, Practices, and Future Roadmap

This article describes how ByteDance tackled petabyte‑scale feature storage by adopting Apache Iceberg, detailing the problem background, design choices, implementation of COW and MOR back‑fill strategies, performance optimizations, and future plans such as lake‑cold‑layering and materialized views.

Apache IcebergBig DataData Lake
0 likes · 16 min read
Scaling ByteDance Feature Store to EB‑Level with Apache Iceberg: Architecture, Practices, and Future Roadmap
DataFunSummit
DataFunSummit
Apr 24, 2023 · Artificial Intelligence

OpenMLDB: A Production‑Grade Feature Platform for Consistent Online and Offline Machine Learning

OpenMLDB is an open‑source machine‑learning database that delivers a production‑grade, consistent online‑offline feature platform for real‑time AI applications such as recommendation, risk control and fraud detection, offering millisecond‑level feature computation, dual SQL engines, extensive ecosystem integration, and a roadmap of new capabilities.

AIFeature StoreOpenMLDB
0 likes · 13 min read
OpenMLDB: A Production‑Grade Feature Platform for Consistent Online and Offline Machine Learning
DataFunTalk
DataFunTalk
Mar 28, 2023 · Artificial Intelligence

FeatHub: An Open‑Source Feature Store for Real‑Time and Offline Feature Engineering

This article introduces FeatHub, an open‑source feature‑store project from Alibaba Cloud that provides a Python SDK, flexible architecture, and execution engines such as Flink and Spark to simplify the development, deployment, monitoring, and sharing of real‑time and offline machine‑learning features across multi‑cloud environments.

Feature StoreFlinkPython SDK
0 likes · 21 min read
FeatHub: An Open‑Source Feature Store for Real‑Time and Offline Feature Engineering
ITPUB
ITPUB
Dec 21, 2022 · Databases

How OpenMLDB Guarantees Real‑Time, Consistent Features for Machine Learning at Scale

This article explains the data and feature engineering challenges of deploying machine learning, introduces OpenMLDB’s open‑source architecture—including offline Spark‑based processing, a high‑availability online engine with dual‑layer memory indexes, snapshot/binlog persistence, and pre‑aggregation techniques—then showcases real‑world case studies and the project’s roadmap.

Feature StoreOpenMLDBReal-time analytics
0 likes · 15 min read
How OpenMLDB Guarantees Real‑Time, Consistent Features for Machine Learning at Scale
DataFunTalk
DataFunTalk
Dec 13, 2022 · Artificial Intelligence

End-to-End Machine Learning Application Using OpenMLDB and Alibaba Cloud MaxCompute

This article demonstrates how to build a complete end-to-end machine-learning workflow for taxi trip duration prediction by integrating OpenMLDB with Alibaba Cloud MaxCompute’s serverless services, covering environment setup, offline data ingestion, feature extraction, model training, deployment, and real-time online inference within 20 ms.

Feature StoreMaxComputeOpenMLDB
0 likes · 13 min read
End-to-End Machine Learning Application Using OpenMLDB and Alibaba Cloud MaxCompute
DataFunTalk
DataFunTalk
Sep 3, 2022 · Databases

OpenMLDB: An Open‑Source Machine Learning Database for Consistent Online and Offline Feature Serving

This article presents OpenMLDB, an open‑source machine learning database that unifies offline and online feature computation with millisecond‑level latency, outlines its development history, architecture, recent 0.6.0 enhancements, ecosystem integrations, and multiple real‑world deployment case studies across finance, banking, research, and marketing domains.

AIFeature StoreOpenMLDB
0 likes · 16 min read
OpenMLDB: An Open‑Source Machine Learning Database for Consistent Online and Offline Feature Serving
NetEase Cloud Music Tech Team
NetEase Cloud Music Tech Team
Jun 29, 2022 · Artificial Intelligence

Music FeatureBox: A Custom Feature Store for Machine Learning at NetEase Cloud Music

Music FeatureBox is NetEase Cloud Music’s custom feature store that centralizes metadata, unifies offline and online feature storage across multiple engines, provides a cross‑language DSL for extraction, ensures training‑inference data consistency, and offers built‑in monitoring, thereby streamlining feature engineering and accelerating the platform’s machine‑learning lifecycle.

DataHubFeature StoreMLOps
0 likes · 17 min read
Music FeatureBox: A Custom Feature Store for Machine Learning at NetEase Cloud Music
Big Data Technology Architecture
Big Data Technology Architecture
Jun 22, 2021 · Databases

Hopsworks Feature Store: Transparent Dual‑Storage System for Online and Offline Machine Learning Features

This article explains how Hopsworks’ feature store unifies online low‑latency and offline high‑throughput storage using a dual‑system architecture built on RonDB, detailing its API, metadata handling, ingestion pipeline, benchmarks, and how it simplifies production machine‑learning feature access.

BenchmarkFeature StoreRonDB
0 likes · 17 min read
Hopsworks Feature Store: Transparent Dual‑Storage System for Online and Offline Machine Learning Features
ITFLY8 Architecture Home
ITFLY8 Architecture Home
Feb 23, 2021 · Artificial Intelligence

How Meituan Built a One‑Stop Machine Learning Platform for Delivery Optimization

This article explains how Meituan’s delivery business has transitioned from data online to AI‑driven decision making by building a comprehensive, one‑stop machine learning platform that includes model management, data graph, feature store, AB testing, and a machine‑learning definition language to accelerate algorithm iteration and reduce operational costs.

AB testingAI PlatformDelivery Logistics
0 likes · 5 min read
How Meituan Built a One‑Stop Machine Learning Platform for Delivery Optimization
vivo Internet Technology
vivo Internet Technology
Mar 18, 2020 · Databases

Vivo Feature Storage Practice: Architecture, Design, and Future Directions Using Nebula Graph

Vivo’s feature‑storage platform, built on Nebula Graph’s Raft‑based, storage‑compute‑separated architecture and exposed via Redis‑compatible proxies, meets massive, low‑latency AI data demands while offering strong consistency, horizontal scalability, backup, active‑active replication, and a roadmap toward general‑purpose KV, cloud‑native integration, and advanced storage engines.

Feature StoreKVNebula Graph
0 likes · 21 min read
Vivo Feature Storage Practice: Architecture, Design, and Future Directions Using Nebula Graph