Will Tensorflow Serving Ease Data Science Operational Pain?

One of the more exciting things I heard lately was Google continuing to open source more and more of the TensorFlow ecosystem with the release of TensorFlow Serving. 


From their site… TensorFlow™ is an open source software library for numerical computation using data flow graphs.

TensorFlow Serving

From their site… TensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data.

It is exciting news to see these types of releases and advances. The operationalization of data science is very challenging for any organization. Managing model builds, versions, deployment and maintenance is extremely challenging technically and procedurally within the organization. These products go part of the way to helping improve this state of affairs and are a welcome addition to the data science tool chain.