end-to-end | BestHub

Old Zhao – Management Systems Only

Jun 10, 2025 · Operations

Why Digital Procurement Fails and How to Build End‑to‑End Collaboration

This article explains why many digital procurement projects fail, identifies five common pitfalls—from neglecting process redesign to siloed systems—and offers a step‑by‑step end‑to‑end collaboration framework that aligns demand, approval, planning, supplier, and data flows for true procurement digitalization.

Digital Transformationcollaborationend-to-end

0 likes · 9 min read

Why Digital Procurement Fails and How to Build End‑to‑End Collaboration

Baidu Tech Salon

Jan 8, 2025 · Artificial Intelligence

Evolution of Video Search Ranking Architecture Toward an End‑to‑End Large‑Model Framework

The paper describes transforming a tightly coupled, multi‑stage video search ranking pipeline into a modular, end‑to‑end large‑model architecture that decouples recall, employs a graph‑engine parallel framework and elastic compute allocation, thereby boosting performance, flexibility, personalization and lowering long‑term operational costs.

Large Language ModelsParallel Computingelastic resources

0 likes · 10 min read

Evolution of Video Search Ranking Architecture Toward an End‑to‑End Large‑Model Framework

ZhongAn Tech Team

Dec 8, 2024 · Artificial Intelligence

Weekly AI Digest Issue 5: Voice Interaction Trends, End‑to‑End vs. Chain Integration, and Enterprise Solutions

This issue examines the growing importance of voice interaction in AI, highlights Justin Uberti’s move to OpenAI and the launch of GPT‑4o, compares end‑to‑end large‑model and chain‑integration approaches, and offers practical enterprise deployment scenarios for both weak and strong voice‑based interactions.

AIChain IntegrationEnterprise Solutions

0 likes · 14 min read

Weekly AI Digest Issue 5: Voice Interaction Trends, End‑to‑End vs. Chain Integration, and Enterprise Solutions

Continuous Delivery 2.0

Aug 22, 2023 · Fundamentals

Why End‑to‑End Testing Strategies Often Fail and How to Build Effective Feedback Loops

The article examines why end‑to‑end testing strategies frequently break in practice, illustrates common pitfalls through a realistic scenario, and proposes a more reliable testing pyramid that emphasizes fast, reliable, isolated unit and integration tests while establishing quick feedback loops for developers.

Testingend-to-endfeedback loop

0 likes · 10 min read

Why End‑to‑End Testing Strategies Often Fail and How to Build Effective Feedback Loops

Bilibili Tech

Feb 28, 2023 · Artificial Intelligence

High‑Quality Automatic Speech Recognition (ASR) Solutions at Bilibili: Data, Model, and Deployment Optimizations

Bilibili’s high‑quality ASR system combines large‑scale filtered business data, semi‑supervised Noisy‑Student training, an end‑to‑end CTC model with lattice‑free MMI decoding, and FP16‑optimized FasterTransformer inference on Triton, delivering top‑ranked accuracy, low latency, and scalable deployment for diverse Chinese‑English video content.

ASRBilibiliSpeech Recognition

0 likes · 18 min read

High‑Quality Automatic Speech Recognition (ASR) Solutions at Bilibili: Data, Model, and Deployment Optimizations

DataFunSummit

Oct 20, 2022 · Artificial Intelligence

End-to-End Speech Relation Extraction

This paper presents an end‑to‑end approach for extracting relational triples directly from speech signals, bypassing intermediate transcription, and demonstrates its effectiveness on synthesized speech versions of the CoNLL04 and TACRED datasets, highlighting challenges such as length constraints and cross‑modal alignment.

Natural Language ProcessingRelation Extractionend-to-end

0 likes · 17 min read

Architects' Tech Alliance

Oct 3, 2022 · Artificial Intelligence

DPU Performance Benchmark Methodology and Implementation (2022)

This article details the DPU performance benchmark testing framework, describing three test system architectures—Single-End, End-to-End, and Multi-End—along with their hardware and software components, test workflow, result reporting requirements, and the necessary tools and drivers for accurate and repeatable performance evaluation.

DPUMulti-EndPerformance Benchmark

0 likes · 9 min read

DPU Performance Benchmark Methodology and Implementation (2022)

58 Tech

Sep 29, 2022 · Artificial Intelligence

End-to-End Speech Recognition Optimization and Deployment at 58.com

58.com’s AI Lab presents a comprehensive overview of its end‑to‑end speech recognition system, detailing data collection, semi‑supervised training, Efficient Conformer architecture, model compression, and deployment strategies that together achieve high accuracy across diverse acoustic conditions and large‑scale production workloads.

AIEfficient ConformerSemi-supervised Learning

0 likes · 19 min read

End-to-End Speech Recognition Optimization and Deployment at 58.com

DataFunTalk

Jul 7, 2022 · Artificial Intelligence

Huawei Translation’s Achievements and Technical Solutions in IWSLT 2022 Speech Translation Tasks

This article reviews Huawei Translation’s top-ranking results in the IWSLT 2022 speech translation competition across speech‑to‑speech, offline speech‑to‑text, and length‑controlled translation tasks, and details their cascade and end‑to‑end technical approaches, including domain‑controlled ASR, context‑aware MT re‑ranking, and VITS‑based TTS.

ASRHuaweiIWSLT

0 likes · 13 min read

Huawei Translation’s Achievements and Technical Solutions in IWSLT 2022 Speech Translation Tasks

Continuous Delivery 2.0

Apr 5, 2022 · Fundamentals

Just Say No to More End-to-End Tests

While end-to-end tests seem appealing by mimicking real user scenarios, they often become unreliable, slow, and costly, leading teams to miss bugs, waste time, and struggle with feedback loops; a balanced testing strategy that emphasizes fast, reliable unit and integration tests, following the test pyramid, yields better quality and faster releases.

Testingend-to-endintegration-testing

0 likes · 14 min read

DataFunSummit

Nov 18, 2021 · Artificial Intelligence

Enterprise Applications and Research of Speech Translation

This article reviews recent advances in speech translation, discusses ByteDance's practical deployments, compares cascade and end‑to‑end modeling approaches, introduces improved encoder‑decoder architectures and training strategies, and reports state‑of‑the‑art results on the IWSLT 2021 benchmark.

AIByteDancecascade model

0 likes · 15 min read

Enterprise Applications and Research of Speech Translation

58 Tech

Aug 19, 2020 · Artificial Intelligence

Speech Recognition in 58.com: Application Scenarios, Data Collection, Kaldi Chain Model Practice, and End‑to‑End Exploration

This article presents a comprehensive overview of how 58.com leverages large‑scale voice data from call‑center, private phone, and micro‑chat platforms, detailing data collection, annotation, Kaldi‑based chain model training, lattice‑free techniques, and end‑to‑end Transformer‑CTC models to improve Chinese speech recognition performance.

ASRKaldiSpeech Recognition

0 likes · 16 min read

Speech Recognition in 58.com: Application Scenarios, Data Collection, Kaldi Chain Model Practice, and End‑to‑End Exploration

FunTester

Mar 16, 2020 · Fundamentals

Common Reasons Why End-to-End Automation Testing Fails and How to Avoid Them

The article outlines why end-to-end test automation often fails—such as hiring the wrong people, neglecting code quality, underestimating long-term effort, misunderstanding automation scope, limited test coverage, and poor visibility—and offers practical guidance to improve automation success.

Automation Testingdevelopment skillsend-to-end

0 likes · 11 min read

Common Reasons Why End-to-End Automation Testing Fails and How to Avoid Them

DataFunTalk

Feb 13, 2020 · Artificial Intelligence

Deep Learning Techniques and Challenges in Autonomous Driving

This article reviews the rapid development of deep learning, its pivotal role in autonomous driving, outlines end‑to‑end perception‑to‑control pipelines, discusses the strengths and limitations of deep models, and proposes practical strategies such as task decomposition, multi‑method fusion, and sensor integration to improve safety and interpretability.

autonomous drivingcomputer visiondeep learning

0 likes · 8 min read

Deep Learning Techniques and Challenges in Autonomous Driving

JD Tech

Dec 6, 2018 · Operations

Shortening Decision Chains: End-to-End Inventory Management and Intelligent Replenishment in JD's Supply Chain

JD's chief scientist Shen Zuo‑jun explains how shortening the decision chain with end‑to‑end algorithms and intelligent multi‑level replenishment dramatically improves inventory turnover, stock availability, and forecasting accuracy, showcasing a novel supply‑chain research direction that integrates AI, big data, and human expertise.

end-to-endforecastinginventory management

0 likes · 9 min read

Shortening Decision Chains: End-to-End Inventory Management and Intelligent Replenishment in JD's Supply Chain

Didi Tech

Jun 1, 2018 · Artificial Intelligence

Didi's Attention-Based End-to-End Mandarin Speech Recognition: A Detailed Review

Didi’s attention‑based end‑to‑end Mandarin speech recognizer, built on the Listen‑Attend‑Spell architecture and modeling roughly 5,000 common characters, delivers 15‑25% relative accuracy gains over its prior LSTM‑CTC system while cutting model size, latency and server requirements and simplifying training by eliminating separate acoustic, pronunciation and language components.

AttentionLASMandarin

0 likes · 14 min read

Didi's Attention-Based End-to-End Mandarin Speech Recognition: A Detailed Review

Architects Research Society

Oct 2, 2016 · Artificial Intelligence

Key Takeaways from Andrew Ng’s Deep Learning Talk at the Bay Area Deep Learning School

The article summarizes Andrew Ng’s presentation at BADLS, highlighting major deep‑learning trends such as the rise of big data, end‑to‑end models, the bias‑variance tradeoff, human‑level performance benchmarks, and practical advice for improving one’s AI skills.

AI Trendsbias-variancedata synthesis

0 likes · 10 min read

Key Takeaways from Andrew Ng’s Deep Learning Talk at the Bay Area Deep Learning School