System Design Patterns for ML Applications

Drawing from my experience as both a software engineer and ML practioner, I’ve learned that building production ML systems requires a unique blend of ML expertise and software engineering best practices.

Key Architectural Components

1. Feature Engineering Pipeline

• Data validation and cleaning
• Feature extraction and transformation
• Feature store for caching and reuse

# Example feature pipeline
class FeaturePipeline:
    def __init__(self, validators, transformers):
        self.validators = validators
        self.transformers = transformers
    
    def process(self, data):
        # Validation
        for validator in self.validators:
            data = validator.validate(data)
        
        # Transformation
        for transformer in self.transformers:
            data = transformer.transform(data)
        
        return data

2. Model Training Infrastructure

Key considerations:

• Experiment tracking
• Model versioning
• Distributed training support
• Hyperparameter optimization

3. Inference Service Architecture

Best practices:

• Model serving with batching
• Caching predictions
• Load balancing
• Monitoring and logging

Common Patterns

1. Feature Store Pattern

   • Centralized feature computation
   • Consistent features across training and inference
   • Version control for features

2. Model Registry Pattern

   • Version control for models
   • Model metadata tracking
   • A/B testing support

3. Prediction Cache Pattern

   • Cache frequent predictions
   • Reduce computational load
   • Handle cache invalidation

Monitoring and Observability

Essential metrics to track:

• Model performance metrics
• System performance metrics
• Data drift metrics
• Resource utilization