Back-of-the-Envelope Resource Estimation for Model Deployment

Before deploying a machine learning model, you must know how much hardware it needs to run efficiently without breaking the bank. Making wild guesses leads to over-provisioning or system crashes. This text-based course teaches you how to perform rapid, back-of-the-envelope calculations to estimate storage, memory, and bandwidth requirements for deployment. You will learn to size infrastructure for large language models, modern vector databases, and traditional machine learning pipelines using simple math. What you'll learn: 1. Calculate GPU VRAM requirements based on model parameters and quantization levels. 2. Estimate storage and memory needs for vector databases in retrieval-augmented generation workflows. 3. Compute network bandwidth requirements for handling concurrent user requests and high-throughput inference. 4. Determine the optimal number of inference servers needed to meet specific latency and throughput targets. 5. Apply quick mathematical frameworks to avoid costly cloud over-provisioning errors. Starting with foundational definitions of model parameters and hardware metrics, you will progress through written practical scenarios, learning to calculate resources for real-world deployment challenges. This course is designed for beginner machine learning engineers, data scientists, and system architects who want to master infrastructure planning without needing prior DevOps experience. Start learning how to plan your deployment infrastructure with confidence today.