MLSys-NYU-2022

Covers real-world ML applications like fraud detection and recommender systems within financial contexts, providing practical case studies not commonly found in generic courses.

Integrates modern tools such as Metaflow, Streamlit, and Comet with hands-on examples and sandbox access, bridging the gap between academic learning and professional workflows.

Organized by week with self-contained folders, READMEs, and reproducible requirements.txt files, making it easy to navigate and replicate the coursework step-by-step.

Emphasizes experiment tracking, pipeline deployment, and evaluation methods using tools like Comet and Flask, preparing users for building reliable ML systems in production.