serving and simple_tensorflow_serving
Serving system B is a lightweight, simplified alternative built on top of the same TensorFlow ecosystem as A, offering easier setup for straightforward inference scenarios where A's high-performance distributed architecture would be overkill.
About serving
tensorflow/serving
A flexible, high-performance serving system for machine learning models
Supports multi-model and multi-version serving with zero-downtime model updates, canary deployments, and A/B testing. Exposes gRPC and REST APIs while featuring a request batching scheduler that groups inference calls for efficient GPU execution with configurable latency bounds. Natively integrates TensorFlow SavedModels but extends to non-TensorFlow models, embeddings, and feature transformations through a modular architecture.
About simple_tensorflow_serving
tobegit3hub/simple_tensorflow_serving
Generic and easy-to-use serving service for machine learning models
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