How deeply does Deep Learning with R, Third Edition cover TensorFlow Serving?

Explains export, reload, and serving, but stops short of server configuration and API implementation details.

TensorFlow Serving

As explained in Deep Learning with R, Third Edition

TensorFlow Serving is a system for serving trained machine-learning models through production prediction APIs.

TensorFlow Serving exposes trained models through prediction APIs by separating model weights and computation graphs from the R or Python application. section 6.3.2

library(keras3)
model |>
  export_savedmodel("path/to/location", format = "tf_saved_model")  #1

reloaded_artifact <- tensorflow::tf$saved_model$load("path/to/location")  #2
predictions <- reloaded_artifact$serve(input_data)

Export a Keras model as a TensorFlow SavedModel, reload the serving artifact, and call its serve function with input data. section 6.3.2

Use this deployment pattern when:

A separate application environment, such as C++ or JavaScript, needs to consume predictions through a REST API. section 6.3.2
The application has reliable internet access, tolerates roughly 500 ms round trips, and can send non-sensitive input data to a server. section 6.3.2

How Deep Learning with R, Third Edition covers it

Cover of Deep Learning with R, Third Edition

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Deep Learning with Python, Third EditionA serving library for deploying TensorFlow computation graphs with saved weights as inference services.

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Related concepts

NVIDIA Triton Inference Servicealternative to · NVIDIA Triton Inference Server is open-source software for deploying and serving machine-learning models through standardized interfaces across frameworks and hardware backends.
ONNXalternative to · ONNX is an open standard for representing machine-learning models in a portable format that supports exchange and execution across frameworks and runtimes.
KServeused by · KServe is an open-source Kubernetes-native platform for deploying and serving machine-learning models through standardized resources and interfaces.
Model Servingused by · A model-serving platform is software infrastructure that hosts trained models and exposes inference by accepting requests and returning model outputs.
TensorFlowuses · TensorFlow is an open-source software platform for numerical computation and machine learning, with tools for developing, training, and deploying models.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.