ML System Design: Back-of-the-Envelope Estimation Techniques

When designing machine learning systems, making rapid, accurate approximations of resource requirements is a critical skill for both engineering interviews and real-world production planning. This text-based course teaches you how to perform quick, back-of-the-envelope calculations without getting bogged down in complex formulas. You will transition from guessing infrastructure needs to confidently estimating model size, storage capacity, network throughput, and training timelines. What you'll learn: - Calculate model memory footprint and storage requirements for both traditional and modern transformer architectures. - Estimate network throughput, latency, and queries-per-second constraints for real-time machine learning APIs. - Compute training time and hardware requirements based on dataset size, batch size, and floating-point operations. - Size vector databases and retrieval-augmented generation pipelines for modern AI applications. - Apply estimation strategies to real-world scenarios, comparing CPU versus GPU memory trade-offs. - Analyze cost implications of storage, compute, and data transfer during the design phase. The course begins with foundational concepts, key terminology, and the core mathematical units of system design. You will then progress through guided written examples and step-by-step breakdowns, analyzing scenarios ranging from basic recommendation engines to modern large-scale language model deployments. Designed specifically for software engineers, data scientists, and aspiring machine learning engineers, this course requires no advanced infrastructure experience. Start reading today to build the quantitative skills needed to design scalable, cost-effective machine learning systems.