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:
: Formulate the problem as a specific ML task, such as binary classification or multi-task learning. Data Preparation & Feature Engineering Candidates who have compared Aminian’s notes to giants
For anyone aiming for machine learning (ML) roles at top-tier tech companies like Meta, Google, or Amazon, the system design round is often the "make or break" stage. While several resources exist, by Ali Aminian and Alex Xu (published by ByteByteGo ) has emerged as a preferred resource. : Defining business goals and system constraints
: Defining business goals and system constraints. He learned to: Clarify Requirements
| Resource | Strength | Weakness | |----------|----------|----------| | | ML-specific frameworks, concise, interview-focused | Less detail on pure infrastructure (e.g., Kubernetes) | | Alex Xu – Vol 2 (ML chapter) | Great diagrams, general system design context | ML depth is limited to a few chapters | | Chip Huyen – Designing ML Systems | Deep, principled, production-focused | Too detailed for interview prep (more for builders) | | Grokking ML System Design (Educative) | Interactive, structured | Paywall, sometimes outdated | | Google’s ML System Design (public guide) | Official, high-level | Not enough for live coding/whiteboard |
to dismantle any vague interview question into a structured plan. The Training Leo spent the next 15 hours immersed in the book's 211 diagrams . He learned to: Clarify Requirements