Model Serving - Deploying ML Models in Production

Model Serving is the process of deploying machine learning models in p

Model serving addresses the challenge of deploying trained models in production environments where they can be accessed by applications and users. Model serving requires handling prediction requests, managing model versions, scaling to handle load, monitoring performance, and ensuring reliability.

Effective model serving is essential for practical AI applications. Model serving approaches include: REST APIs for HTTP-based access, gRPC for efficient communication, batch processing for bulk predictions, real-time serving for low-latency inference, edge deployment for local inference, and cloud serving for scalable deployment. Each approach offers different trade-offs between latency, throughput, and complexity.

MLflow provides model serving capabilities for models tracked in MLflo

ONNX Runtime provides cross-platform model serving for ONNX models. ONNX Runtime enables: framework-agnostic serving, optimization, and diverse deployment options. ONNX Runtime is valuable for cross-platform deployment.

Serving Infrastructure

Optimize performance using: model optimization (quantization, pruning)

Monitoring and Observability

Implement monitoring for: latency, throughput, error rates, resource usage, and model performance. Monitoring enables detection of issues and optimization. Comprehensive monitoring supports reliable serving.

Frequently asked questions