Machine Intelligence at Google Scale: Vision/Speech API, TensorFlow and Cloud Machine Learning

The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn't scale for production service, so you need a Distributed Training on the cloud eventually. Google has been building infrastructure for training the large scale neural network on the cloud for years, and now started to share the technology with external developers. In this session, we will introduce new pre-trained ML services such as Cloud Vision API and Speech API that works without any training. Also, we will look how TensorFlow and Cloud Machine Learning will accelerate custom model training for 10x - 40x with Google's distributed training infrastructure.

Kaz Sato is Staff Developer Advocate at Cloud Platform team, Google Inc. He leads the developer advocacy team for Machine Learning and Data Analytics products, such as TensorFlow, Vision API and BigQuery, and speaking at major events including Strata+Hadoop World 2016 San Jose, Google Next 2015 NYC and Tel Aviv and DevFest Berlin. Kaz also has been leading and supporting developer communities for Google Cloud for over 7 years. He is also interested in hardwares and IoT, and has been hosting FPGA meetups since 2013.