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。
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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 SDCA algorithm) 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。
