tomevault-io 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).
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