ML Serving Frameworks

Moving a model from a notebook to production requires more than just an API endpoint.

Key Challenges

• Dynamic Batching: Grouping requests to maximize GPU/CPU throughput.
• Model Graphing: Chaining pre-processing, inference, and post-processing steps.
• Autoscaling: Adjusting resources based on traffic.

Frameworks

• BentoML: Packaging models as OCI-compliant containers.
• Ray Serve: A scalable model serving library built on Ray.
• FastAPI: For simple, lightweight model microservices.

Best Practices

• Inference Optimization: Use ONNX or TensorRT to accelerate predictions.
• Monitoring: Track latency, throughput, and system health (CPU/Memory).