system engineering

iQIYI Technical Product Team

Oct 10, 2024 · Artificial Intelligence

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

iQIYI’s minute‑level online deep‑learning framework overcomes stability, timeliness, compatibility, delayed feedback, catastrophic forgetting, and i.i.d. constraints through high‑availability pipelines, TensorFlow Example serialization, rapid P2P model distribution, flexible scheduling, disaster‑recovery rollbacks, PU‑loss adjustment, and knowledge‑distillation, delivering a 6.2% revenue boost.

CTR predictionadvertisingdeep learning

0 likes · 9 min read

Online Deep Learning (ODL) for Real‑Time Advertising Effectiveness: Challenges and Solutions

Model Perspective

Mar 6, 2024 · Fundamentals

Why Managing a City Is Like Designing a Spaceship: Exploring Complex Systems

An insightful look at how both spacecraft design and city governance exemplify complex systems, distinguishing closed versus open systems, outlining characteristics of complex and mega-complex systems, and linking these concepts to system engineering pioneers like Qian Xuesen and modern large language models.

Qian Xuesencomplex systemslarge language models

0 likes · 9 min read

Why Managing a City Is Like Designing a Spaceship: Exploring Complex Systems

Architects Research Society

Jul 4, 2023 · Fundamentals

Design Patterns and Interface Coupling in Enterprise Service Environments

The article explains the concept of software design patterns, their role in managing coupling and interfaces, and provides best‑practice guidelines for enterprise‑level service design, illustrating how MITRE system engineers should select and apply patterns to achieve loosely‑coupled, extensible systems.

design patternsenterprise architectureinterface coupling

0 likes · 13 min read

Design Patterns and Interface Coupling in Enterprise Service Environments

ByteDance SYS Tech

Nov 15, 2022 · Cloud Native

How ByteDance’s STE Team Tackles Linux Kernel Memory Waste and Drives Cloud‑Native Innovation

This interview reveals how ByteDance’s STE team, a youthful group of Linux kernel engineers, identified and solved memory‑management redundancy, contributed the HVO and VDUSE projects to the open‑source community, and leveraged these advances to boost cloud‑native performance and reliability across the company.

Linux kernelVirtualizationcloud-native

0 likes · 15 min read

How ByteDance’s STE Team Tackles Linux Kernel Memory Waste and Drives Cloud‑Native Innovation

Alimama Tech

Dec 22, 2021 · Artificial Intelligence

Performance Optimization of Advertising Deep Learning Systems: Algorithm, System, and Hardware Co‑Design

The paper presents a holistic algorithm‑system‑hardware co‑design for advertising deep‑learning inference, combining model pruning, approximate computing, kernel fusion, scheduling and PCIe transfer optimizations with GPU and NPU upgrades, achieving up to five‑fold speed‑up and significantly higher latency‑bounded QPS for large‑scale ad services.

Algorithmic OptimizationGPUNPU

0 likes · 24 min read

Performance Optimization of Advertising Deep Learning Systems: Algorithm, System, and Hardware Co‑Design

JD Tech Talk

Jun 5, 2020 · Information Security

From System Engineering to Anti‑Black‑Market: Lin Yuansheng’s Journey in JD’s Intelligent Risk Detection

Lin Yuansheng, JD’s first post‑doctoral researcher, leverages his system‑engineering background and big‑data analytics to build models that identify black‑market accounts, improve network risk perception, and demonstrate how academic research can be rapidly applied to real‑world e‑commerce security challenges.

Big DataData ModelingInformation Security

0 likes · 8 min read

From System Engineering to Anti‑Black‑Market: Lin Yuansheng’s Journey in JD’s Intelligent Risk Detection

Art of Distributed System Architecture Design

Oct 16, 2015 · Artificial Intelligence

Building Machine Learning Systems in Small Teams: Practices, Pitfalls, and Lessons from Dangdang

This talk shares the experience of a small machine‑learning team at Dangdang, describing how they built a recommendation system from scratch, the tools and processes they used, the challenges of limited personnel, and the many pitfalls they encountered while iterating toward a production‑ready solution.

Best PracticesML pipelineTechnical Debt

0 likes · 21 min read

Building Machine Learning Systems in Small Teams: Practices, Pitfalls, and Lessons from Dangdang