Machine Learning in Production

Deploying machine learning models to production is vastly different from training them. Here's what you need to know to build robust ML systems.

The Production Challenge

Training a model is just the beginning. Production systems must handle:

• Scale: Serving thousands of predictions per second
• Reliability: Maintaining high availability
• Monitoring: Detecting model degradation
• Updates: Deploying new versions safely

Architecture Patterns

Model Serving

Choose the right serving strategy:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()
model = load_model("production-model-v1")

class PredictionRequest(BaseModel):
    features: list[float]

@app.post("/predict")
async def predict(request: PredictionRequest):
    prediction = model.predict([request.features])
    return {"prediction": prediction[0]}

Batch vs Real-Time

Data Pipeline Management

Robust data pipelines are critical:

1. Data Validation: Ensure input quality
2. Feature Engineering: Transform raw data
3. Feature Store: Centralize feature management
4. Versioning: Track data and model versions

Feature Store Example

class FeatureStore:
    def get_features(self, entity_id: str, features: list[str]):
        """Retrieve features for an entity"""
        return self.storage.query(
            entity_id=entity_id,
            features=features,
            timestamp="latest"
        )
    
    def write_features(self, entity_id: str, features: dict):
        """Write computed features"""
        self.storage.insert(
            entity_id=entity_id,
            features=features,
            timestamp=now()
        )

Monitoring and Observability

Track these critical metrics:

def monitor_predictions(y_true, y_pred):
    accuracy = calculate_accuracy(y_true, y_pred)
    
    if accuracy < threshold:
        alert("Model accuracy below threshold")
        trigger_retraining()

Continuous Training

Keep models fresh with automated retraining:

1. Detect performance degradation
2. Collect new training data
3. Retrain and validate model
4. Deploy with A/B testing
5. Monitor new version

A model in production is never finished—it's continuously evolving.

Best Practices

Deployment Strategies

Blue-Green Deployment

Run two identical production environments:

services:
  model-blue:
    image: ml-model:v1
    replicas: 3
  
  model-green:
    image: ml-model:v2
    replicas: 3

routing:
  production: blue  # Switch to green after validation

Canary Releases

Gradually roll out new versions:

• Start with 5% of traffic
• Monitor metrics closely
• Increase to 25%, 50%, 100%
• Rollback if issues detected

Production ML requires a different mindset than research. Focus on reliability, monitoring, and continuous improvement to build systems that deliver value over time.