| Feature | Generic University PDF | Ali Aminian’s "Better" PDF | | :--- | :--- | :--- | | | Academic proofs & math | Interview storytelling & trade-offs | | Diagram | Generic DAG (Directed Acyclic Graph) | Interview-ready whiteboard flows | | Trade-offs | "L1 vs L2 regularization" | "Batch inference vs. real-time for ad latency" | | The "Whitespace" | Ignores hardware (GPUs) & serving | Dedicated section on Feature Store & Model Registry | | Case Studies | Wine quality or Iris dataset | Uber ETA, DoorDash delivery time, TikTok For You |
: It provides a reliable 7-step framework designed specifically for the flow of an interview, helping candidates avoid getting lost in ambiguous questions. | Feature | Generic University PDF | Ali
: Provides a consistent template for solving any ML design problem, covering everything from clarifying requirements to monitoring in production. 10 Real-World Case Studies 10 Real-World Case Studies If you obtain a
If you obtain a legitimate copy of his material (or the next best thing), do this: Aminian provides verbatim scripts for how to explain
Let’s settle the debate. Compared to the industry standard "Machine Learning System Design Interview" by Alex Xu (which is great), where does Ali Aminian’s PDF fit?
Candidates who have compared Aminian’s notes to giants like Alex Xu ( System Design Interview – An Insider’s Guide ) or Chip Huyen ( Designing Machine Learning Systems ) often point to three distinct advantages in Aminian’s PDF:
Other PDFs mention this. Aminian provides verbatim scripts for how to explain solving this using patterns or feature validation .