Model Serving Architecture
Scalable infrastructure for serving machine learning models in production with high availability and low latency.
High ComplexityTechnologies & Tools
TensorFlow ServingTorchServeSeldon CoreKubernetesRedisNginx
Architecture Flow
1
Request Reception
Receive inference requests via API
REST APIgRPCGraphQLLoad Balancer
2
Data Preprocessing
Transform input data for model consumption
Data ValidationFeature EngineeringNormalization
3
Model Inference
Execute model prediction
Model RuntimeGPU/CPU OptimizationBatch Processing
4
Post-processing
Transform model output and return response
Output FormattingConfidence ScoringResponse Caching
Use Cases
Real-time predictions
Recommendation systems
Computer vision APIs
Natural language processing
Fraud detection
Pros
Low latency inference
High throughput
Automatic scaling
Model versioning
A/B testing support
Cons
Resource intensive
Model loading overhead
Complex deployment
Monitoring challenges
Cost optimization
When to Use
Real-time inference
High throughput requirements
Production ML systems
API-based ML services
Low latency needs
Alternatives
Batch processingCloud ML servicesEdge deploymentCustom serving
Performance Metrics
Latency
Very Low (milliseconds)
Throughput
Very High (thousands of requests/sec)
Scalability
Excellent
Reliability
High
Cost
Medium to High
Key Trade-offs
Latency
Optimized for low-latency inference
Resource Usage
Models loaded in memory for fast access
Scalability
Horizontal scaling with load balancing
Category Information
Category
Model Serving
Complexity Level
High