原文地址
Machine Learning in the Cloud, with TensorFlow
Wednesday, March 23, 2016
用TensorFlow在云端进行机器学习
At Google, researchers collaborate closely with product teams, applying the latest advances in Machine Learning to existing products and services - such as speech recognition in the Google app,search in Google Photos and the Smart Reply feature in Inbox by Gmail - in order to make them more useful. A growing number of Google products are using TensorFlow, our open source Machine Learning system, to tackle ML challenges and we would like to enable others do the same.
在Google，研究人员和产品团队密切协作，把最新的机器学习的进展融入到了现有的产品和服务中，比如：Google应用中的语音识别、Google照片的查找和Gmail收件箱的智能回复特征，这都是为了让这些产品和服务更加的实用。Google产品中使用TensorFlow（我们开源的机器学习系统）的数量正在增加，同时为了掌控机器学习的挑战，我们将确保更多的产品使用TensorFlow.
Today, at GCP NEXT 2016, we announced the alpha release of Cloud Machine Learning, a framework for building and training custom models to be used in intelligent applications.
今天，在GCP NEXT 2016上，我们宣布正式发布云机器学习的alpha版本，它是一个框架，这个框架将用来构建和训练客户模型，这些客户模型将应用在人工智能应用程序中。
Machine Learning projects can come in many sizes, and as we’ve seen with our open source offering TensorFlow, projects often need to scale up. Some small tasks are best handled with a local solution running on one’s desktop, while large scale applications require both the scale and dependability of a hosted solution. Google Cloud Machine Learning aims to support the full range and provide a seamless transition from local to cloud environment.
机器学习项目可以是各种大小的，就像我们已经看到的我们提供的开源的TensorFlow，项目通常需要去向上扩展。一些运行在个人的电脑上的本地解决方案的小项目是最容易掌握的；与此同时，大规模的应用需要较大的规模和hosted依赖的解决方案。Google的云机器学习，目标是为了支持全领域的解决方案，并且提供一个从本地到云环境的无缝过度。
机器学习项目可以是各种大小的，就像我们已经看到的我们提供的开源的TensorFlow，项目通常需要去向上扩展。一些运行在个人的电脑上的本地解决方案的小项目是最容易掌握的；与此同时，大规模的应用需要较大的规模和hosted依赖的解决方案。Google的云机器学习，目标是为了支持全领域的解决方案，并且提供一个从本地到云环境的无缝过度。
The Cloud Machine Learning offering allows users to run custom distributed learning algorithms based on TensorFlow. In addition to the deep learning capabilities that power Cloud Translate API,Cloud Vision API, and Cloud Speech API, we provide easy-to-adopt samples for common tasks like linear regression/classification with very fast convergence properties (based on the SDCAalgorithm) and building a custom image classification model with few hundred training examples (based on the DeCAF algorithm).
云机器学习提供了允许用户在TensorFlow的基础上运行客户的分布式学习算法的功能。在深度学习的容量之上，增强了Cloud Translate API,Cloud Vision API, and Cloud Speech API，我们提供了一些易于用于常用任务中的例子，比如：采用非常快的趋于一致属性的linear regression/classification（基于SDCA算法）和用几百个训练例子构建一个客户图像分类模型
（基于DeCAF算法）。
We are excited to bring the best of Google Research to Google Cloud Platform. Learn more about this release and more from GCP Next 2016 on the Google Cloud Platform blog.
我们非常兴奋的把Google研究的最好的内容带到了Google云平台上。希望更多的了解这次发布和GCP Next 2016更多的内容，可以到Google云平台博客。