研究表明，机器学习在公共部门的成功，将更少依赖模型准确率的突破，而更多依赖机构能否构建出透明、可复现、可问责且受公民信任的数据基础设施。It is shown that the success of machine learning in the public sector will depend less on breakthroughs in model accuracy and more on the ability of institutions to engineer transparent, reproducible, and accountable data infrastructures that citizens can trust.
论文
2 张论文卡片 · 工程化 · 观点
条目 G: 公共部门 ML Pipeline 工程教训（含性能数据表）
16. [arxiv:2004.05074 — Paxos vs Raft: Have we reached consensus on distributed consensus?](https://arxiv.org/abs/2004.05074)
16. Paxos vs Raft：分布式共识是否已经达成共识？
本文探讨 Paxos 与 Raft 何者更优地解决分布式共识问题，通过以 Raft 的术语与实用抽象描述一个简化的 Paxos 算法，精确揭示两者的差异。This paper considers the question of which algorithm, Paxos or Raft, is the better solution to distributed consensus to determine exactly how they differ by describing a simplified Paxos algorithm using Raft's terminology and pragmatic abstractions.