: A quick-reference article that outlines the core framework needed to clarify requirements and translate abstract problems into ML solutions. Recommended 9-Step Interview Framework
: Based on Chip Huyen’s Stanford course, this is a deep dive into the engineering side of ML, covering data pipelines and monitoring.
: Focuses on the end-to-end lifecycle of ML systems in production, bridging the gap between theory and practical deployment.
: This is one of the most comprehensive resources, featuring a 9-Step ML System Design Formula
Here are some GitHub repositories to help you prepare: