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.

In LoRA, what actually gets trained during fine-tuning?

Only two small low-rank matrices (A and B) injected into chosen layers, while the base stays frozen, LoRA freezes the entire base model and adds two small matrices A and B next to chosen weight matrices. Only A and B receive gradients, which is why trainable parameters drop to well under one percent.

What specific problem does QLoRA solve that plain LoRA does not?

It quantizes the frozen base to 4-bit so the base itself takes far less memory, letting the whole fine-tune fit on one small GPU, Plain LoRA still holds the frozen base in fp16 (about 14 GB for a 7B model). QLoRA quantizes that frozen base to 4-bit NF4, shrinking it to roughly 4 GB, so a 7B fine-tune fits on a single consumer 24 GB GPU.

Why does the adapter approach make serving many fine-tuned tasks cheaper?

Because one shared base model stays in GPU memory and tiny task adapters are swapped on top per request, Each task is just a small adapter, not a full model. One expensive base sits in VRAM and adapters (tens of megabytes each) are hot-swapped per request, so many fine-tuned behaviors share a single loaded base instead of running separate full copies.

, engineers learning right now

Aryan from Bengaluru unlocked lifetime access · 3 minutes ago

Parameter-Efficient Fine-Tuning: LoRA and QLoRA

Name: System Design Masterclass
Availability: InStock

in Llm Genai Ops

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