Cloud Datalab: Jupyter with the power of BigQuery and TensorFlow
45 min
Data engineers, data analysts and data scientists
Beginner
In this session we will explain how you can use Google Cloud Datalab, a Jupyter environment from Google that integrates BigQuery, TensorFlow, and other Google Cloud services seamlessly. You can easily run SQL queries from Jupyter to access terabytes of data in seconds, and train a deep learning model with TensorFlow with tens of GPUs in the cloud.
Cloud Datalab is a version of Jupyter Notebook that is tightly integrated with Google Cloud services. It is a one stop shopping for data analytics and machine learning. The tool comes with the standard data analytics libraries such as TensorFlow, scikit-learn, numpy and matplotlib and so on. Also, Cloud Datalab integrates the powerful suite of Google Cloud data services, including Cloud ML Engine (fully managed and scalable platform for TensorFlow), BigQuery (data warehouse), Cloud Dataflow (batch and stream processing) and Cloud Storage (object storage), Stackdriver (monitoring). In this session, we will learn how Cloud Datalab can dramatically increase the productivity and shorten the time for the preprocess-train-validate lifecycle for data analytics.
Jupytercon video (O'Reilly account required)