Is this a video course?

No. This is an interactive, slide-based learning platform. Each lesson has rich text, animated diagrams, live code editors, and quizzes. You learn by reading, interacting, and doing, not by watching videos passively.

How long do I have access?

Forever. Both pricing tiers are one-time payments with lifetime access. This includes all current 766 lessons and any future content we add.

What level of experience do I need?

None. We start from absolute basics like 'What is latency?' and build up to distributed consensus protocols. The Foundation level assumes zero prior knowledge of system design.

How much does the system design course cost?

7.99 US dollars for lifetime access globally, or 499 Indian rupees for lifetime access in India. One-time payment, no subscription, no hidden fees. 11 lessons are free with no signup required.

What technologies are covered?

Everything from DNS and load balancers to Kubernetes, Kafka, distributed databases, consensus protocols, stream processing, security architecture, and observability. We cover principles and real-world implementations used at Netflix, Google, Amazon, Uber, Stripe, and more.

Is this useful for system design interview preparation?

Yes. The lessons are structured around the exact topics asked in system design interviews at FAANG and top-tier companies. Interactive diagrams help you practice whiteboard-style explanations. Covers everything from URL shortener design to distributed payment systems.

How is this different from ByteByteGo or Educative?

766 interactive lessons (4x more than most competitors), 16 different diagram types that build step by step, real production examples from Netflix, Google, Amazon, Uber, and Stripe, and lifetime access for a one-time payment of $7.99 instead of annual subscriptions costing 100 to 200 dollars per year.

Two rubrics on the same 40 traces and the same two graders both gave raw agreement of 0.93, but kappa of 0.69 and 0.82. How is that possible?

With only two choices, most answers land in 'correct', so agreeing by luck is much easier and kappa subtracts more, Raw agreement counts matches and nothing else, and the graders matched on 37 of 40 traces under both rubrics. Kappa asks how many of those matches luck would have produced. Under the two-choice rubric 36 of 40 answers went into 'correct', so two graders with those habits match 76% of the time by luck. With 'partial' available the answers spread out and chance agreement falls to 58%. Same matches, different amount of luck, different kappa. This is exactly why raw agreement cannot be used to compare two rubrics.

Two graders label a set where 90% of answers are correct. Both answer 'correct' every time without reading. What do raw agreement and kappa report?

Raw 0.90 and kappa 0.00, They match on 90% of traces, so raw agreement is 0.90 and sounds excellent. Kappa asks how much agreement two graders with those habits would get by chance, which here is all of it, and returns zero. That gap is exactly why kappa is reported.

Your confusion matrix shows both disagreements on the correct/partial boundary and none on correct/wrong. What does that tell you to do?

Rewrite the definition that separates correct from partial, then re-measure, The matrix points at one boundary. The rubric is clear about what counts as a failure and vague about degree, so exactly one definition needs rewriting. Going back to two choices would hide the vagueness rather than fix it.

A colleague reports their LLM judge achieves 0.95 agreement with human labels. The two humans agree with each other at kappa 0.82. What should you conclude?

Ask which human it was measured against, and what it scores against the other one, Be careful here, because the obvious answer is wrong. A judge CAN agree with grader A 95% of the time when A and B only agree at 0.82; nothing forbids it. What it cannot do is score high against both at once, since whatever it gains with A it gives back with B. So a single high number is not impossible, it is incomplete: the useful question is which grader it was measured against and what it scores against the other. Note also that 0.95 'agreement' and a kappa of 0.82 are two different measurements, so comparing them directly is a second mistake.

Why is running the SAME model twice a poor way to measure inter-annotator agreement?

It measures sampling noise at temperature, not whether the rubric is well defined, The point of a second annotator is to discover boundaries your rubric never defined. One model run twice shares every habit with itself, so where it misreads an unclear sentence it will tend to misread it the same way. Two different vendors is the cheap honest stand-in.

Inter-Annotator Agreement: Before You Trust Your Own Labels

Name: System Design Masterclass
Availability: InStock

in Evals

This lesson is part of the AI Engineering course. Unlock the full course, a separate one-time payment, for lifetime access.

One-time payment. Lifetime access. No subscription.

Global

$20/lifetime

One-time payment via PayPal

Get Lifetime Access

5 Foundation lessons free, try before you buy

India Special

India

₹999/lifetime

One-time via UPI, cards, or net banking

Get Lifetime Access

5 Foundation lessons free, try before you buy

“The interactive diagrams alone are worth way more than the price.”

Anish K., Senior Engineer at a Bengaluru startup

Every ML Platform lesson, Foundation to Advanced

Real architecture diagrams with real tool logos

MLOps, LLMOps, and data engineering in depth

Quizzes with instant feedback

Built and reviewed by senior engineers

Lifetime access, all future content included

The first 5 Foundation lessons of this course are free, no payment needed. Try them now