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

Foundations

• ml-systems-fundamentals - Core ML systems concepts
• deep-learning-primer - Deep learning foundations
• dnn-architectures - Neural network architectures
• deployment-paradigms - Deployment patterns

Data Engineering

• data-engineering - Data pipelines and quality
• training-data - Training data management
• feature-engineering - Feature creation and stores

Model Development

• ml-workflow - ML development workflow
• model-development - Model training and selection
• ml-frameworks - Framework best practices

Performance

• efficient-ai - Efficiency techniques
• model-optimization - Quantization, pruning, distillation
• ai-accelerators - Hardware acceleration

Deployment

• model-deployment - Production deployment
• inference-optimization - Inference optimization
• edge-deployment - Edge and mobile deployment

Operations

• mlops - ML operations and lifecycle
• robust-ai - Reliability and robustness

Key Principles

1. Data-Centric AI - Focus on data quality over model complexity
2. Iterative Development - Start simple, iterate based on metrics
3. Production-First - Design for deployment from the start
4. Monitoring - Continuous monitoring and improvement
5. Reproducibility - Version everything (data, code, models)

References

• Harvard CS 329S: Machine Learning Systems Design
• Designing Machine Learning Systems by Chip Huyen
• MLOps: Continuous Delivery and Automation Pipelines