Google open-sources TensorFlow Serving for deploying machine learning models
Image Credit: Eduardo Woo/Flickr
Google today announced that it has open-sourced TensorFlow Serving, a piece of software that makes it easy to deploy machine learning models that can make inferences about new data. The software, which is available on GitHub, works natively with Google’s previously open-sourced TensorFlow deep learning framework, but it can also support other tools.
- inferences - 推論
“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,” Google software engineer Noah Fiedel wrote in a blog post.
Written primarily in C++, the technology should make it a little easier for people to get off the ground when serving up machine learning models using open source tools such as TensorFlow. And while TensorFlow Serving is flexible, because it natively supports TensorFlow, it could help boost adoption of that framework from Google. As more developers start to use the TensorFlow software, Google could improve its capabilities and even uncover new talent.
最初はC++で記述されたこの技術は、TensorFlowのようなオープンソースのツールを使用する学習モデルを提供するときには、だれでも使えるように、少し簡単にならなくてはなりません。 そして、TensorFlow Serving は、TensorFlowをネイティブサポートしているので、フレキシブルです。 グーグルによるフレームワークの採用を後押しできます。多くの開発者がTensorFlowを使い始めるので、グーグルはその能力、新たな網羅されていない能力でさえ、改善できてるはずです。
Deep learning is increasingly popular, not only at web companies like Google and Facebook, but also among startups, as it can help with image recognition, speech recognition, and natural language processing. The process involves training artificial neural networks on large sets of data and then having them make inferences about new data. The TensorFlow serving software is specifically geared toward the inference phase.
An overview of the architecture of TensorFlow Serving is here.