Machine Learning

TensorFlow-Serving: Flexible, High-Performance ML Serving

Tagged: ,

This topic contains 0 replies, has 1 voice, and was last updated by  arXiv 11 months, 3 weeks ago.


  • arXiv
    5 pts

    TensorFlow-Serving: Flexible, High-Performance ML Serving

    We describe TensorFlow-Serving, a system to serve machine learning models inside Google which is also available in the cloud and via open-source. It is extremely flexible in terms of the types of ML platforms it supports, and ways to integrate with systems that convey new models and updated versions from training to serving. At the same time, the core code paths around model lookup and inference have been carefully optimized to avoid performance pitfalls observed in naive implementations. Google uses it in many production deployments, including a multi-tenant model hosting service called TFS^2.

    TensorFlow-Serving: Flexible, High-Performance ML Serving
    by Christopher Olston, Noah Fiedel, Kiril Gorovoy, Jeremiah Harmsen, Li Lao, Fangwei Li, Vinu Rajashekhar, Sukriti Ramesh, Jordan Soyke
    https://arxiv.org/pdf/1712.06139v2.pdf

You must be logged in to reply to this topic.