# Experiment Design Frameworks

## Overview

World-class experiment design frameworks for senior data scientist.

## Core Principles

### Production-First Design

Always design with production in mind:
- Scalability: Handle 10x current load
- Reliability: 99.9% uptime target
- Maintainability: Clear, documented code
- Observability: Monitor everything

### Performance by Design

Optimize from the start:
- Efficient algorithms
- Resource awareness
- Strategic caching
- Batch processing

### Security & Privacy

Build security in:
- Input validation
- Data encryption
- Access control
- Audit logging

## Advanced Patterns

### Pattern 1: Distributed Processing

Enterprise-scale data processing with fault tolerance.

### Pattern 2: Real-Time Systems

Low-latency, high-throughput systems.

### Pattern 3: ML at Scale

Production ML with monitoring and automation.

## Best Practices

### Code Quality
- Comprehensive testing
- Clear documentation
- Code reviews
- Type hints

### Performance
- Profile before optimizing
- Monitor continuously
- Cache strategically
- Batch operations

### Reliability
- Design for failure
- Implement retries
- Use circuit breakers
- Monitor health

## Tools & Technologies

Essential tools for this domain:
- Development frameworks
- Testing libraries
- Deployment platforms
- Monitoring solutions

## Further Reading

- Research papers
- Industry blogs
- Conference talks
- Open source projects
