Data Science and machine learning are unlocking new value in our data. A quickly evolving ecosystem of powerful open-source tools coupled with big data systems like Apache Hadoop can drive better model accuracy and deliver more strategic analytic capabilities. However today, most data science teams work away from the Hadoop cluster, often on their laptops or in data silos. This limits the data that can be used in data science research and creates operational overhead and gaps in security.
In this talk, Sean will provide background and a demo of the Data Science Workbench. He'll introduce an enterprise data science platform that accelerates analytics projects from exploration to production. Sean will explore why collaborative, customizable, self-service access is critical for data scientists to secure Hadoop environments via Python, R, and Scala.