While other books give you sample solutions, Aminian provides a repeatable framework. His PDF breaks down any MLSD question (e.g., “Design a Recommendation System for YouTube”) into four immutable steps:
This framework is what interviewers at FAANG look for. It shows you are systematic, not lucky.
Is the PDF perfect? No. Critics note that the original version lacks deep dives into cost calculation (e.g., $ per 1K predictions on AWS vs. GCP) and can be light on modern orchestration tools like Flyte or Ray. Furthermore, because it is a self-published PDF, the visual diagrams are sometimes less polished than those in a retail book.
However, for the majority of senior-level interviews, the signal-to-noise ratio of Aminian’s material is unmatched. It is not a beginner’s guide to Python or a stats refresher. It assumes you know the basics and cuts straight to the system design case studies.
A common question: "Does Ali Aminian’s framework work for Generative AI (RAG, Fine-tuning, Agents)?"
Yes—and this is why it is "better." He updated his curriculum in late 2023/2024 to include:
If you find an older PDF (pre-2022), it is still 80% valid for classical ML (Ranking, Forecasting, Anomaly Detection). For GenAI, look for his "ML System Design for LLMs" supplement.
Most guides start with the infrastructure (Kubernetes, Kafka). Aminian starts with the data. He forces you to ask:
By anchoring the design in the statistical properties of the data, the architecture becomes an emergent property of the problem, not a pre-baked template.
Ali Aminian is a seasoned Machine Learning Engineer (formerly at Uber and Lyft) and a prolific interview coach. While he has multiple formats (courses, blogs, YouTube), the PDF you are searching for is likely a distillation of his ML System Design Interview Roadmap.
Why do users append the word "better" to his name? Because his framework directly addresses the three fatal flaws listed above.
Unlike other resources that jump straight into writing code or drawing boxes, Aminian forces you to solve the problem logically before drawing a single arrow. His "better" approach is based on these six pillars: