ml-systems

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. Use when this capability is needed.
metadata	{"author":"doanchienthangdev"}

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

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