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A learning-focused repository for system design, focused on understanding how large-scale systems are structured, why specific architectural choices are made, and what trade-offs those decisions introduce.

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System Design in Machine Learning

This repository is my personal space for learning and understanding system design, starting from the basics. I created this repo to document how I think about system design, not to dump finished answers or production-ready code. Most of the content here focuses on ideas, diagrams, and reasoning rather than implementations.

What This Repo Is About

  • Understanding how systems are structured
  • Learning why certain design choices are made
  • Exploring trade-offs instead of “perfect solutions”
  • Breaking down complex ideas into simple explanations

How I’m Learning System Design

My approach is:

  • Learn concepts before tools
  • Focus on thinking and reasoning, not memorization
  • Write things in a way I can understand later
  • Keep notes simple and interview-friendly

If I can explain something clearly in this repo, I know I’ve actually understood it.

How Notes Are Written

Most notes try to answer simple questions like:

  • What problem does this solve?
  • How does it work at a high level?
  • When should I use this?
  • When should I avoid it?
  • What are the trade-offs?

I also use diagrams and tables whenever they explain things better than text.

About Code

There is very little code in this repository.

When code appears, it’s only:

  • Small examples
  • Pseudocode
  • Something that helps clarify an idea

This repo is about design thinking, not implementation details.

Who This Repo Might Help

  • Beginners starting system design
  • Students preparing for interviews
  • Anyone who wants a slower, clearer introduction to system design

If this helps future-me or someone else reading it — that’s a win.

Status

This is an ongoing learning repo.
Notes may change, improve, or get rewritten as I understand things better.

About

A learning-focused repository for system design, focused on understanding how large-scale systems are structured, why specific architectural choices are made, and what trade-offs those decisions introduce.

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