ML/AI System: Architectural Patterns
ML systems stopped being standalone experiments years ago; they're now production components with the same reliability and scaling demands as any other backend, plus failure modes of their own. This is a reading list of engineering write-ups we recommend to candidates preparing for ML system design interviews, with a note on what each one teaches.
ML/AI Case Studies
1. How LinkedIn Detects Spam Content
LinkedIn's spam detection pipeline combines NLP over post content, ML models trained on spam patterns, heuristics for suspicious behavior, and human moderation workflows to keep false positives in check. A good template for any trust-and-safety design question.
2. How Spotify Generates Ad Content at Scale
Spotify's infrastructure for generating personalized marketing content automatically across formats and platforms: the content pipeline, targeting systems, and what they automated versus kept human.
An overview of the training process behind ChatGPT: data curation, the model architecture, distributed training, and the fine-tuning techniques that turned a base model into an assistant.
4. How Airbnb Discovers What Users Like
Airbnb's approach to learning guest preferences and prioritizing listing attributes, a concrete example of recommendation systems built from real user-interest signals.
5. How Microsoft Diagnoses Production Issues with LLMs
Microsoft Research on using large language models for cloud incident management: automating root cause analysis and mitigation suggestions, and how well LLMs actually perform at it.
How Coaches Can Help
ML system design sits at the intersection of software engineering, data, and operations, and interviews probe all three. A coach with production ML experience helps you practice the full loop: framing the problem, designing the training and serving paths, planning for monitoring and drift, and defending your trade-offs under follow-up questions.
Conclusion
Each of these write-ups pairs an ML problem with the unglamorous engineering that makes it work in production: pipelines, moderation loops, observability. That pairing is exactly what ML system design interviews look for, so read these with as much attention to the plumbing as to the models.