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.
Data Science Experience Local is available now from IBM Marketplace. For more information visit the IBM website