ML Systems

Building production-ready machine learning systems.

Overview

This skill category covers the complete ML system lifecycle:

1. Foundations - Core concepts, architectures, paradigms
2. Data Engineering - Data collection, quality, feature engineering
3. Model Development - Training, evaluation, frameworks
4. Performance - Optimization, acceleration, efficiency
5. Deployment - Serving, edge deployment, scaling
6. Operations - MLOps, monitoring, reliability

Categories