ml-system-design

What problem does it solve?

Designing robust, production-ready ML systems that prevent training-serving skew, data leakage, and reproducibility issues across teams.

Core Features & Use Cases

• Guidance on architecture patterns (FTI pipeline, data contract first, parity between training and inference).
• Best practices for feature stores, monitoring, model versioning, and reproducibility.
• Use cases include designing end-to-end ML pipelines, architecture reviews, and anti-pattern reviews for ML systems.

Quick Start

Outline a production ML system design for a real-time inference service using FTI pipelines and a feature store.