NVIDIA Triton Inference Server for production ML serving — dynamic batching, concurrent model execution, multiple backends, and the GPU-utilization and model-management problems it solves between a trained model and serving at scale.
Triton
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Triton Inference Server: Production ML Serving That Actually Scales -
MLOps Fundamentals: Experiment Tracking, Model Registry, Serving, and Drift Monitoring A practical guide to MLOps — structuring experiments with MLflow, managing the model lifecycle through a registry, serving models in production with BentoML and Triton Inference Server, and detecting data and concept drift before it silently degrades your models.