Kazunori Sato explains how you can use Google Cloud Datalab—a Jupyter environment from Google that integrates BigQuery, TensorFlow, and other Google Cloud services seamlessly—to easily run SQL queries from Jupyter to access terabytes of data in seconds, train a deep learning model with TensorFlow with tens of GPUs in the cloud, and apply your models to the dataset with a little work, with all the usual tools available on Jupyter.
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 and Hadoop World 2016, and Google Next 2015 NYC and Tel Aviv, and he has hosted FPGA meetups since 2013.
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