Deployment of a TensorFlow model to Production using TensorFlow Serving

Deployment of a TensorFlow model to Production using TensorFlow Serving

Deploy a Deep Learning Model to Production using TensorFlow Serving.

Learn step by step deployment of a TensorFlow model to Production using TensorFlow Serving.

You created a deep learning model using Tensorflow, fine-tuned the model for better accuracy and precision, and now want to deploy your model to production for users to use it to make predictions.

TensorFlow Serving allows you to

  • Easily manage multiple versions of your model, like an experimental or stable version.
  • Keep your server architecture and APIs the same
  • Dynamically discovers a new version of the TensorFlow flow model and serves it using (remote procedure protocol) using a consistent API structure
  • Consistent experience for all clients making inferences by centralizing the location of the model

The key components of TF Serving are

  • Servables: A Servable is an underlying object used by clients to perform computation or inference**. TensorFlow serving represents the deep learning models as one ore more Servables.
  • LoadersManage the lifecycle of the Servables as Servables cannot manage their own lifecycle. Loaders standardize the APIs for loading and unloading the Servables, independent of the specific learning algorithm.
  • Source: Finds and provides Servables and then supplies one Loader instance for each version of the servable.
  • Managers: Manage the full lifecycle of the servable: Loading the servable, Serving the servable, and Unloading the servable
  • TensorFlow Core: Manages lifecycle and metrics of the Servable by making the Loader and servable as opaque objects

tensorflow-serving deep-learning mnist tensorflow windows-10

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