ML System Design

This section translates theoretical machine learning models into robust, scalable, and production-ready architectures for real-world use cases.

Key Focus Areas

• Problem Formulation: Translating business metrics into ML objectives.
• Data & Feature Pipelines: Real-time vs. batch feature stores, handling data drift and skew.
• Scalability & Trade-offs: Latency, throughput, storage constraints, and cost optimization for inference.