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Dataiku Releases Data Science Studio Update

By Casey Houser
April 04, 2016

Dataiku, a developer of visual application development software, recently announced the release of Data Science Studio 3, an update to the DSS platform that gives users an updated graphical environment for their creation of data science applications.

DSS 3 brings a host of new features to the table. It begins with real-time model deployment that features use of an API, improved data monitoring, security and quality checks for integration with other enterprise software, and resource usage monitoring to provide a comprehensive look at the development software’s performance in a various settings.

Dataiku tries to work around the problem of development environments being disconnected from production. Instead of fitting naturally into commercial product releases, the company notes, businesses may find that their development teams create applications and then have no control over them once they have been released to production settings. A common situation – such as in-house developers creating a marketing service for its counterparts on the company floor below – may have taken a turn for the worse in a recent update, but DSS tries to fix that problem by fitting the design, build, run, test, deployment, and roll-back stages of the application lifecycle into production.

Businesses can release a product and continue to see how it functions for their clients or, in the case above for in-house teams, and if something goes wrong with an initial release of updated version, developers can take quick action to fix the problem by creating further updates or by rolling back to a stable version.

This new software begins by giving data scientists the ability to connect to any data from local file stores, cloud storage, multiple types of databases, and even social media. Analysts can create build predictive models to share with others by taking advantage of the DSS API. The API allows other applications, such as those used in the marketing department, to link to DSS in meaningful ways. TMC will take a look at similar features as it progresses in its All About the API event this year.

Updates to the data monitoring capabilities in DSS take this one step further by allowing data scientists to define “data boundaries” that can mark low or high levels of certain types of business factors. When a data type steps beyond these bounds, DSS automatically generates an alert.

Together, the inclusion of security and quality checks with resource usage monitoring gives DSS users the ability to see how applications are performing in the real world. DSS can keep its eye on the quality of data to make sure it is properly used within various company departments, and it can show how production applications are making use of computing systems – CPU resource use, bandwidth, and drain on specific databases or cloud systems. All told, the updates try to bring entire businesses together by making their interactions through data science applications meaningful and productive.

Edited by Maurice Nagle

Contributing Writer

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