TensorFlow Serving
TensorFlow Serving is an open-source system for deploying trained machine-learning models and exposing them through production inference APIs.
Deep Learning with Python, Third Edition
A serving library for deploying TensorFlow computation graphs with saved weights as inference services.
- How it is used
- Deploys TensorFlow inference models as remotely accessible REST services for applications outside Python.
- How deep it goes
- Defines graph-and-weights serving and demonstrates export, reload, and prediction, but defers server configuration and operational internals.
- Examples
- Uses Keras export, TensorFlow SavedModel reload, and serving predictions as the concrete workflow.
- What you can do after
- Apply it via Keras export to package a model for remote inference outside the Python application.
Read how Deep Learning with Python, Third Edition explains TensorFlow Serving →
Deep Learning with R, Third Edition
TensorFlow Serving is a system for serving trained machine-learning models through production prediction APIs.
- How it is used
- Deploys trained models as remotely accessible prediction APIs for production applications.
- How deep it goes
- Explains export, reload, and serving, but stops short of server configuration and API implementation details.
- Examples
- Uses a Keras SavedModel export, TensorFlow reload, and serve call as the concrete deployment example.
- What you can do after
- Apply it via Keras and TensorFlow APIs to export a model artifact and obtain predictions from the reloaded artifact.
Read how Deep Learning with R, Third Edition explains TensorFlow Serving →
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.
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