AI Model Deployment: Strategies and Platforms

Deployment Strategies

Choosing the right deployment strategy depends on your requirements for latency, scale, and infrastructure.

Cloud Deployment

Managed Services

AWS SageMaker

Google Vertex AI

Azure ML

Scalable and managed

Self-Managed

Kubernetes clusters

Docker containers

More control, more work

Edge Deployment

Benefits

Low latency

Offline capability

Data privacy

Reduced bandwidth

Challenges

Limited compute

Model optimization required

Update management

Technologies

TensorFlow Lite

ONNX Runtime

PyTorch Mobile

Core ML

Containerization

Docker

Consistent environments

Easy scaling

Version control

Kubernetes

Orchestration

Auto-scaling

Load balancing

Serving Patterns

Real-Time Serving

REST APIs

gRPC

Low latency requirements

Batch Inference

Scheduled jobs

Large-scale processing

Cost-efficient

Streaming

Event-driven

Real-time data

Kafka/Spark integration

Model Optimization

Quantization

Pruning

Distillation

Compilation (TVM, TensorRT)

Considerations

Latency requirements

Scale needs

Cost constraints

Update frequency

Security requirements

Conclusion

The right deployment strategy balances performance, cost, and operational complexity.