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.

On the lab data, merging near-duplicate codes changed the top category from 'answer absent' (3) to 'answer is incomplete' (6). Why?

'Incomplete' had been written five different ways, and each spelling was counted separately, Incomplete, incomplete_answer, missing_details, missing details and vague all describe the same failure. Counted separately, none was larger than 2. Counted together, the category is 6, twice the size of anything else and previously invisible.

Two codes read 'incomplete' and 'ignored the given context'. Both produce a poor answer. Should they merge?

No, because they are repaired by different changes in different components, The test is the repair, never the wording. One is fixed by asking the model for every requested field, the other by checking the context reaches the model. Merged, they become a single number that two different teams would each read as being about their work.

Why does the lesson insist the merge mapping is written to a file rather than done in a spreadsheet by eye?

It makes the one step that is a matter of opinion reviewable, re-runnable on new traces, and reversible in one line, Everything else in this chapter is arithmetic on recorded fields. The merge is a person deciding two phrases mean the same thing, so it is the step most worth exposing. As a file, a colleague objects to one line instead of to your conclusions, and the next hundred traces merge identically.

Two independent graders produced 13 and 15 raw codes with almost no shared vocabulary. After merging, one had four categories and the other three, and they disagreed about which was biggest. What does that tell you?

The taxonomy is not stable yet, so read more traces or write a clearer definition before building any metric on it, This is the useful kind of disappointing result. One grader named a failure kind the other never saw, and the two disagreed about which category was biggest: for A, 'answer is incomplete' leads at 6 traces while 'ignored the given context' sits at 3; for B those two are tied at 9 each. A team reading only one chart would start on different work. That is a signal to keep reading, not a signal to pick a winner. Note also that both graders' codes were merged through the SAME mapping file, so agreement on category NAMES would have proved little. The counts are the part the shared mapping could not force, and the counts disagree.

A code appears exactly once and does not fit any category you have built. What should you do with it?

Leave it as its own entry, Forcing it somewhere destroys the only signal it was carrying, and deleting it is worse. Many singletons are the first sighting of a category you have not read enough traces to see yet, which is exactly what the saturation curve is there to tell you.

Axial Coding: Turning Eleven Words Into Four Categories

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