MLOps in Practice: Deploying and Managing ML Models at Scale

Why MLOps Matters

Building a model is the easy part. Deploying, monitoring, retraining, and managing versions. That's where MLOps comes in. Without proper MLOps, models degrade silently and lose accuracy over time.

The MLOps Stack We Use

Data Pipeline

• Data Versioning: DVC for tracking dataset versions alongside code
• Feature Store: Feast for centralized feature management and sharing
• Data Validation: Great Expectations for automated quality checks

Model Serving

• Inference Server: NVIDIA Triton for GPU-optimized serving
• API Gateway: Kong for routing, rate limiting, and authentication
• Edge Deployment: ONNX Runtime for lightweight inference on edge devices

A/B Testing for ML Models

We route traffic between model variants using a hash-based router that ensures consistent user experience.

class ModelRouter:
    def __init__(self, models, traffic_split):
        self.models = models
        self.traffic_split = traffic_split
    
    def predict(self, request):
        user_hash = hash(request.user_id) % 100
        cumulative = 0
        for variant, split in self.traffic_split.items():
            cumulative += split * 100
            if user_hash < cumulative:
                return self.models[variant].predict(request.features)

Monitoring Key Metrics

Conclusion

MLOps is an evolving practice. Start with basic experiment tracking and model versioning, then gradually add monitoring and automated retraining as your ML systems mature.