December 2018
Intermediate to advanced
764 pages
18h 18m
English
book
Python: Advanced Guide to Artificial Intelligence
by Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani
Content preview from Python: Advanced Guide to Artificial Intelligence
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TensorFlow Models in Production with TF Serving
The TensorFlow models are trained and validated in the development environment. Once released, they need to be hosted somewhere to be made available to application engineers and software engineers to integrate into various applications. TensorFlow provides a high-performance server for this purpose, known as TensorFlow Serving.
For serving TensorFlow models in production, one would need to save them after training offline and then restore the trained models in the production environment. A TensorFlow model consists of the following files when saved:
- meta-graph: The meta-graph represents the protocol buffer definition of the graph. The meta-graph is saved in files with the .meta extension.
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