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 the core problem a model registry exists to solve?

Knowing with certainty which model version is live, what produced it, and how to roll back, A registry is the single source of truth for versions, metrics, lineage, and stages. Its central job is answering which version is in Production and enabling a fast, audited rollback.

In MLflow, what happens to the previous Production model when you promote a new version to Production with archive_existing_versions set to true?

It is moved to the Archived stage, kept intact as a rollback target, Promoting a new version auto-archives the old one. Archived does not mean deleted. The previous model stays fully intact, which is exactly what makes rolling back to a known-good version take seconds.

Why does model lineage (data snapshot, git commit, hyperparameters) matter in a registry?

It lets you trace any live prediction back to its origins and reproduce the exact model later, Lineage links a version to the exact data, code, and parameters that created it. That makes production models reproducible byte for byte and answerable to audits, instead of a mystery you reverse-engineer from Slack.

, engineers learning right now

Aryan from Bengaluru unlocked lifetime access · 3 minutes ago

Model Registry and Versioning: Knowing What Is Actually Live

Name: System Design Masterclass
Availability: InStock

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