It is the central unit in the architecture of TensorFlow Serving, which serves as objects used by the clients in the process of computations. It offers flexible size and granularity. It consists of one lookup table to a tuple of interference models.
Typical servables include the following :
* a TensorFlow SavedModelBundle (tensorflow::Session)
* a lookup table for embedding or vocabulary lookups
It improves the functionality and performance with the help of improvements:
* streaming results
* experimental APIs
* asynchronous modes of operation
TensorFlow serving allows the system of TensorFlow, which we use for machine learning. It allows us to create, use, and execute the algorithms for the system a user wants to build. It extends its functionality with the help of collaborating environments such as TensorBoard.