来自研究一线的实践笔记。
由工程师和研究人员撰写的深度文章，介绍 AI 投入生产后哪些做法有效，以及哪些问题容易被忽视。
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每篇文章都探讨机器学习运维、预测、计算机视觉、语言系统、信息检索和 AI 治理的实际应用。完整文章目前以英文提供。
The future of disaster response: AI's impact on emergency relief
Where AI genuinely helps emergency relief — damage mapping, demand forecasting, multilingual triage — and where human judgement must stay in charge.
Five MLOps mistakes we keep seeing in 2025
From feature-store sprawl to dashboards no one looks at — what we'd do differently after a decade of shipping models into production.
Why your demand forecast is biased (and what to do about it)
A practical playbook for spotting and removing systematic forecast bias — drawn from a decade of forecasting engagements.
Choosing between classical CV and deep learning
When OpenCV beats a neural net — and how to know in advance, before you have invested six months in the wrong stack.
Aspect-based sentiment without breaking the bank
A small-model recipe for accurate, fast, multilingual aspect sentiment that does not require a frontier LLM in the hot path.
Build vs buy in 2025: the new rules
How the rise of foundation models has rewritten the build-vs-buy calculus — and the rules that have stopped being true.
Reproducibility patterns we actually use
The minimum viable practices that make experiments trustworthy — drawn from systems we have been running since 2014.
RAG in the enterprise: what works in 2025
Retrieval-augmented generation has matured. Here is the architecture we now reach for by default — and the failure modes we still see weekly.
The AI Act, for practitioners who have to ship anyway
What the EU AI Act actually requires of high-risk and limited-risk AI systems — and the documentation we have been writing on every project since 2023.
The quiet comeback of small models
The 7-billion-parameter and smaller models we deploy in 2025, the gaps they fill, and why the cost story alone is not the most interesting one.
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