Tagged articles

ML pipeline

3 articles · Page 1 of 1

Jun 5, 2026 · Artificial Intelligence

ml-evolve: Multi‑Agent Self‑Evolving System Built on Real‑World ML Pitfalls

ml-evolve addresses the shortcomings of generic agent‑search frameworks for machine‑learning pipelines by introducing four specialized agents, staged data gating, and cost‑saving mechanisms, and demonstrates its advantages with a two‑tower retrieval case study and concrete performance metrics.

AutoMLML pipelineOptuna

0 likes · 14 min read

ml-evolve: Multi‑Agent Self‑Evolving System Built on Real‑World ML Pitfalls

360 Tech Engineering

Aug 22, 2018 · Artificial Intelligence

Rules of Machine Learning: 43 Practical Guidelines for Building Robust ML Systems

This article translates and summarizes Martin Zinkevich’s “Rules of ML”, offering 43 concise, experience‑based recommendations that cover terminology, pipeline design, feature engineering, monitoring, training‑serving consistency, and model iteration to help engineers build reliable machine‑learning‑driven products.

ML pipelineModel Monitoringbest practices

0 likes · 35 min read

Rules of Machine Learning: 43 Practical Guidelines for Building Robust ML Systems

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.

ML pipelinebest practicesrecommendation

0 likes · 21 min read

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