Pipeline friction in AI model serving — covering export failures, unsupported ops, dynamic input shapes, and version mismatches — can be systematically eliminated with 18 concrete best practices. Key recommendations include validating ONNX exports in CI/CD, using TensorRT dynamic optimization profiles, writing custom TensorRT plugins for unsupported operations, pinning full dependency stacks with NGC containers for reproducibility, and profiling with trtexec and Nsight tools. For production serving, NVIDIA Dynamo-Triton (formerly Triton Inference Server) handles dynamic batching, model versioning, and canary deployments alongside TensorRT engines.
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What is pipeline friction in AI model serving?How to solve model export issuesHow to handle unsupported operationsHow to manage dynamic input sizesHow to prevent version mismatchesHow to profile and debug your pipelineHow to integrate TensorRT with Dynamo-TritonMore tips for establishing a friction-free pipelineGet started eliminating pipeline friction71 Impressions