Feature Store | BestHub

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.

Batch QueryFeature StoreRedis

0 likes · 9 min read

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

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 DataFeature Store

0 likes · 17 min read

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

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.

Artificial IntelligenceBig DataFeature Engineering

0 likes · 19 min read

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

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

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 PlatformCost OptimizationFeature Store

0 likes · 14 min read

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

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 DataFeature Store

0 likes · 16 min read

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

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.

AIData EngineeringFeature Store

0 likes · 13 min read

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

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 EngineeringFeature StoreFlink

0 likes · 21 min read

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

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 StoreMaxComputeOnline Inference

0 likes · 13 min read

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

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.

AIDatabaseFeature Store

0 likes · 16 min read

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

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 StoreMonitor

0 likes · 17 min read

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

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.

Data EngineeringFeature StoreRonDB

0 likes · 17 min read

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

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.

DatabaseDistributed StorageFeature Store

0 likes · 21 min read

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