IBM Launches Data Science Experience
May 10, 2017
has released 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.
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.