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360 Tech Engineering
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 pipelinebest practicesfeature engineering
0 likes · 35 min read
Rules of Machine Learning: 43 Practical Guidelines for Building Robust ML Systems
Art of Distributed System Architecture Design
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 practicesmachine learning
0 likes · 21 min read
Building Machine Learning Systems in Small Teams: Practices, Pitfalls, and Lessons from Dangdang