Preparing for technical interviews often requires reviewing both
system design concepts and machine learning fundamentals.
To keep things organized, I compiled a structured guide that
covers topics commonly asked in interviews.
The guide includes:
• System design fundamentals (load balancing, caching, distributed systems)
• Architecture patterns used in scalable systems
• Machine learning interview concepts
• ML system design and deployment basics
The goal is to provide concise explanations and practical patterns
that engineers can review quickly before interviews.
You can find the repository here:
https://github.com/Ali-Meh619/System_Design_ML_Principles
If you have suggestions for additional topics or improvements,
I would really appreciate feedback.
Top comments (1)
Great resource! One underrated ML interview topic: prompt engineering and structured prompting — increasingly asked at companies building LLM products. Knowing how to structure a prompt systematically (role, context, constraints, output format) is now a genuine engineering skill.
I built flompt (flompt.dev) to make this tangible: paste any prompt, get it decomposed into 12 semantic blocks, then recompiled into structured XML. Useful for learning and for production. Free, open-source.