ARMONK, N.Y. – 10 May 2017: IBM (NYSE: IBM) today announced the availability of a collaborative workspace for private clouds geared towards organizations and data scientists working with sensitive data. Using Data Science Experience Local, data scientists are now able to more easily and quickly collaborate on analytic models and deliver insights that developers can use to build intelligent applications.
Increasingly, data scientists are faced with having to work with mountains of data that are pulled into servers and data centers. For some organizations, moving that data to the cloud for greater access and management isn’t an option due to such constraints as volume, siloed systems and compliance requirements.
Data Science Experience Local is a completely self-contained and resides within an organization’s own servers and data center. Based on the IBM Data Science Experience, which runs in the public cloud, Local comes with all the necessary software to run and manage the development environment, including local installations of Apache Spark and Object Storage in addition to Data Science Experience services. It runs in Kubernetes, an open source cluster manager that provides a scalable, clustered installation of Data Science Experience with many features that are useful for a private cloud platform, such as service monitoring, administration and high availability.
Data scientists working with sensitive data in industries ranging from healthcare to finance, as well as those operating with specific on-premises data management requirements, will be able to use the new solution for collaboration as well as analyzing data from within their own networks more quickly and easily.
Like the public cloud version, Data Science Experience Local enables data scientists to share projects and code, and collaborate and build models using such tools as H2O Libraries, RStudio, Jupyter Notebooks on Apache Spark. Users also can integrate into this open framework models created in IBM SPSS predictive analytics software, for even greater capabilities and insights. Prior to this release, if data science teams did not want to leverage the cloud, they would have to install and manage open source tools individually in silos, or skip them completely due to security and compliance requirements.
“Industries from healthcare to financial services, demand greater rigor around the ingestion, sharing and analyzing of their critical data,” said Rob Thomas, General Manager, IBM Analytics. “With the new local version of the Data Science Experience, data scientists now have a collaborative development environment from within a private cloud setting to quickly and securely extract valuable insights in order to make strategic, data-driven decisions.”
When the Local edition is paired with Data Science Experience, organizations will have a unique holistic approach to data science collaboration that enables scientists to work from anywhere – whether in public or private cloud environments – to create innovative analytic models and intelligent apps. Additionally, they’ll be able to build hybrid solutions, from which they can develop models in the public cloud to run locally, or vice versa.
The SETI Institute (Search for Extra-Terrestrial Intelligence) has incorporated the Data Science Experience and other IBM analytics tools into its observation processes to help it better monitor signals between planets and stars to gauge the existence of life.
“Collaboration among data scientists is something the discipline needs to advance ideas, suggestions and models, rapidly and easily,” said Bill Diamond, President and CEO of the SETI Institute. “The IBM solution takes that concept to a new level and enables our scientists to share complex documents, live code, and equations more quickly and easily with partnering scientists from IBM, Stanford University, and other institutions. Based on the benefits we’ve seen to date, I can only see work like this blossoming even further.”
Data Science Experience Local furthers IBM’s commitment to putting data first and helping organizations gain value through extracting the insights and knowledge they need for better decision making. Building on its $300 million investment in Apache Spark, IBM launched the Data Science Experience last November to extend the speed and agility of Spark to more than two million members of the R community through new contributions to SparkR, SparkSQL and Apache SparkML.