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summary
Describes building reliable TensorFlow inference pipelines with concrete tooling: automated performance tests, memory-profiling and root-cause techniques that find and fix leaking models, and deployment patterns that hit low-latency SLOs in production. Details include using Scala-centric stacks for model serving and an onnx/ONNX integration story borrowed from Stripe, plus workflow automation so engineers push models without owning infra. Claims measurable gains in reliability and latency and shows that the fixes actually resolve recurring failures.
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