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.

What is train and serve skew?

When a feature is computed one way during training and a different way at serving time, so the model sees different values, Skew happens when the same feature is defined or computed differently in training versus serving. The model then receives values that do not match what it learned on, and predictions degrade silently. A feature store prevents this by making both sides read from one definition.

Why does a feature store keep the same feature in both an offline store and an online store?

Because training needs cheap scans over huge history while serving needs the latest value in milliseconds, and no single store is good at both, Training scans large history offline and tolerates slow queries, so it uses a cheap columnar store like S3 Parquet. Serving needs one current value inside the request path, so it uses a fast key-value store like Redis. Same feature, two stores tuned for opposite jobs.

What does a point-in-time join prevent when building a training set?

It prevents label leakage by attaching each training row only the feature values that existed at that event's timestamp, A point-in-time join joins features to each labeled event as of that event's own timestamp. This stops future feature values from leaking into past training rows, which would otherwise make the model look great offline and fail in production where that future information is not available.

, engineers learning right now

Aryan from Bengaluru unlocked lifetime access · 3 minutes ago

Feature Stores: Killing the Train and Serve Skew Bug

Name: System Design Masterclass
Availability: InStock

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This lesson is part of the full System Design Masterclass. Unlock it along with 765 other lessons for a one-time payment.

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