---
name: ml-systems
description: Machine Learning Systems - comprehensive knowledge for building production ML systems from data engineering through deployment and operations. Based on Harvard ML Systems course and Designing ML Systems by Chip Huyen.
---

# 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