Google Delivers TensorFlow Serving, Advancing Machine Learning

Google has now officially released TensorFlow Serving to the open-source community on the heels of open sourcing TensorFlow, a possibly hugely influential contribution to the field of machine learning. As we've noted, artificial intelligence and machine learning are going through a mini-renaissance right now. For example, Facebook is open sourcing its machine learning system designed for artificial intelligence (AI) computing at a large scale. It's based on Nvidia hardware. And, IBM announced that its proprietary machine learning program known as SystemML will be freely available to share and modify through the Apache Software Foundation.

TensorFlow, though, is the actual engine behind several Google tools you may already use, including Google Photos and the speech recognition found in the Google app. Here is more on what TensorFlow Serving adds to the mix.

TensorFlow technology could also be leveraged by researchers who need to analyze very large sets of complex data, according to Google.

According to a Google post:

"Today, we announce the release of TensorFlow Serving, designed to address some of these challenges. TensorFlow Serving is a high performance, open source serving system for machine learning models, designed for production environments and optimized for TensorFlow."

"TensorFlow Serving is ideal for running multiple models, at large scale, that change over time based on real-world data, enabling:

model lifecycle management

experiments with multiple algorithms

efficient use of GPU resources

"TensorFlow Serving makes the process of taking a model into production easier and faster. It allows you to safely deploy new models and run experiments while keeping the same server architecture and APIs. Out of the box it provides integration with TensorFlow, but it can be extended to serve other types of models."

'TensorFlow Serving uses the (previously trained) model to perform inference - predictions based on new data presented by its clients," the post adds. "Since clients typically communicate with the serving system using a remote procedure call (RPC) interface, TensorFlow Serving comes with a reference front-end implementation based on gRPC, a high performance, open source RPC framework from Google."

According to Google, TensorFlow could help speed up processes ranging from drug discovery to processing astronomy-related data sets. Vincent Vanhoucke is an engineer who has worked on TensorFlow, and he writes:

"Very proud to be open-sourcing TensorFlow, Google's newest Deep Learning framework! TensorFlow is both a production-grade C++ backend, which runs on Intel CPUs, NVidia GPUs, Android, iOS and OSX, and a very simple and research-friendly Python front-end that interfaces with Numpy, iPython Notebooks, and all the familiar Python-based scientific tooling that we love. TensorFlow is what we use every day in the Google Brain team, and while it's still very early days and there are a ton of rough edges to be ironed out, I'm excited about the opportunity to build a community of researchers, developers and infrastructure providers around it. Try it out!"

 The TensorFlow team adds:

"TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well."

 The basic goal with most machine learning tools is to take a vast quantity of data and reduce it to manageable, actionable insights. TensorFlow, in all likelihood, will branch out as an open source tool into forks that can be applied for these types of tasks and more. This toolset will be interesting to follow, and TensorFlow Serving will expand the audience for it.