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Cloudera Announces General Availability of Data Science Workbench


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Cloudera, which last week began trading on the New York Stock Exchange under the symbol "CLDR,” has announced the general availability of the Cloudera Data Science Workbench, a self-service tool for data scientists.

The workbench, which was announced in beta at Strata+Hadoop World San Jose 2017, enables fast, easy and secure self-service data science for the enterprise.

The Data Science Workbench is a self-service tool that accelerates the ability to build, scale, and deploy machine learning solutions, said Charles Zedlewski, senior vice president, Products at Cloudera, who noted that this will enable data scientists to share, collaborate and manage their data in a way that best suits them and their enterprise, and result in an easier and faster path to production.

With Python, R, and Scala directly in the web browser, Cloudera Data Science Workbench delivers a self-service data science experience. It gives users the ability to download and experiment with the latest libraries and frameworks in customizable project environments. Cloudera Data Science Workbench is both secure and compliant, with support for Hadoop authentication, authorization, encryption, and governance.

The workbench integrates with many deep learning frameworks, including BigDL, a deep learning library for Apache Spark, open sourced by Intel. Built from the ground-up to run on distributed Spark/Hadoop infrastructure and performance-optimized to run on Intel Xeon processors (leveraging the Intel Math Kernel Library), BigDL works directly within Cloudera's Data Science Workbench.

According to Cloudera,  the benefits of BigDL integration into the Data Science Workbench include the ability to leverage deep learning libraries and tactics on CPU architecture without any additional hardware considerations or separate environments. The combination provides a convenient way to create Spark data science pipelines natively and integrate them with deep learning library (BigDL) and other Spark/Hadoop components on the Cloudera Data Science Workbench.

For more information, go to cloudera.com.


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