---
title: "More Design Patterns For Machine Learning Systems"
url: https://daily.dev/posts/more-design-patterns-for-machine-learning-systems-uswnxndsq
source_url: https://eugeneyan.com/writing/more-patterns
type: article
source: "Eugene Yan"
published: 2026-05-31T07:40:23.278Z
updated: 2026-05-31T08:03:26.592Z
tags: ["machine-learning", "data-science", "design-patterns", "mlops"]
reading_time: 16
upvotes: 1
comments: 0
language: en
---

> ## Documentation Index
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# More Design Patterns For Machine Learning Systems

**[Eugene Yan](https://daily.dev/sources/eugeneyan)** · 16 min read · 1 upvotes · 0 comments

## Summary

Nine reusable design patterns for production ML systems are covered: process data once (and the feature store extension), human-in-the-loop (HITL) labeling with a note that LLMs may replace crowdworkers, data augmentation and synthetic data generation, hard negative mining with curriculum learning, problem reframing (regression↔classification, n-class to binary), cascade architectures (retrieval→ranking→rules), the data flywheel virtuous cycle, business rules layers on top of model outputs, and evaluate-before-deploy with time-based validation splits. Each pattern is illustrated with real-world examples from companies like Meta, Twitter, DoorDash, Shopify, Netflix, Tesla, and OpenAI.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://eugeneyan.com/writing/more-patterns>

---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#data-science](https://daily.dev/tags/data-science), [#design-patterns](https://daily.dev/tags/design-patterns), [#mlops](https://daily.dev/tags/mlops)

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