Grokking the Machine Learning System Design Interview

Machine learning is changing what companies expect from senior engineers. Building a model is only one part of the job. Senior engineers are expected to design ML systems that scale in production while accounting for data quality, infrastructure, latency, reliability, cost, and business requirements. ML system design is a core skill for senior AI and machine learning roles. I created this course based on my experience designing large-scale systems at Microsoft and Meta, where I worked on infrastructure and real-time analytics, and interviewed hundreds of candidates. The biggest pattern I saw was that even strong engineers struggled to structure ambiguous ML system design problems and communicate trade-offs clearly. This course introduces the 6-step framework I developed to solve that gap. In this course, you'll master a 6-step ML system design framework that covers everything from problem formulation and requirements gathering to data strategy, model architecture, evaluation, and production deployment. You'll work through real-world case studies spanning recommendation systems, fraud detection, semantic search, content moderation, and LLM-powered applications, tackling the exact challenges faced in interviews. If you're serious about mastering ML system design interviews, this is the best place to start.