The largest challenge for deep learning is scalability. With a single GPU server, it takes hours or days to finish training. This doesn’t scale for production service; eventually you’ll need distributed training in the cloud. Google has been working on a large-scale neural network in the cloud for years and has now started sharing the power with developers.
Kazunori Sato introduces pretrained ML services, such as the Cloud Vision API and the Speech API, and explores how TensorFlow and Cloud Machine Learning can accelerate custom model training 10–40x with Google’s distributed training infrastructure.
Kaz Sato is a staff developer advocate on the cloud platform team at Google, where he leads the developer advocacy team for machine learning and data analytics products such as TensorFlow, the Vision API, and BigQuery. Kaz has been leading and supporting developer communities for Google Cloud for over seven years. He’s a frequent speaker at conferences, including Google I/O 2016, Hadoop Summit 2016 San Jose, Strata + Hadoop World 2016, and Google Next 2015 NYC and Tel Aviv, and he has hosted FPGA meetups since 2013.
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