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DataScience.com Rolls Out Extensive Update to Enterprise-Ready Data Science Platform

DataScience.com has rolled out extensive new features to accommodate the requirements of enterprise data science teams including deployment of  the platform on-premise and across multiple public cloud providers. The latest release expands enterprise support for data scientist’s tools of choice,  such as Jupyter and RStudio, in addition to support for Python, R, Spark, and SAS. With the platform, data science teams can also now optimize  workflows by grouping code and model outputs into projects integrated with version control tools like Github.

According to a commissioned study conducted by Forrester Consulting on behalf of DataScience.com- only 22% of companies are “Insights Leaders”  who embed data science into their operating models to drive significant business value. DataScience.com’s new release helps organizations realize  value in their investments by giving data science teams the tools, libraries, and programming languages of choice— which are often open-source  —supported by scalable infrastructure, security, and customizable workflows that make it easy to deploy results into production and share insights across departments.

Organizations across the board now understand the importance of scaling data science and the cost of failing to do so,” said DataScience.com CEO  Ian Swanson. “Our platform is designed to help data science teams work more collaboratively and effectively. Now, with our on-premise offering, your  team can leverage their favorite data science tools and workflows in a platform hosted in your data center.”

The features included in the newest release are designed to level-up data science work in a number of key areas, from environment management to model deployment. Benefits include:

  • Flexible deployments: The platform can now be deployed on-premise, or through cloud providers such as Amazon Web Services, Google Cloud,  or Microsoft Azure. New features focused on IT operations enable easy installation, monitoring, and maintenance.
  • Improved project organization and collaboration among teams: Platform users can organize data, code, and model outputs into projects, making  it easier to locate relevant files. The platform also removes the need to configure new environments for every project; instead, users can  set up repeatable, standardized environments with Docker or other containers.
  • Faster and higher-value analyses: Data scientists can work in their code editors of choice and run that code seamlessly in the platform.  Infrastructure can be scaled up or down depending on the demands of an analysis.
  • More visible results: Jupyter notebooks, R Markdown documents, and other files associated with a project can be published as sharable reports.  Code versioning allows teams to track changes and project milestones, so work doesn’t get lost.
  • Streamlined production: Code can be run on a schedule to automate common data science tasks like data cleaning and model retaining, as well  as deployed behind a REST API for easy integration with real-time applications or dashboards.

Our new release speaks to many of the best practices we’ve established for removing barriers to data science excellence,” Swanson added. “In many  cases, the work data scientists are doing is underutilized — either because it’s not getting in front of decision makers, or there aren’t enough  engineering resources to put that work into production. The platform eliminates these issues by putting the entire data modeling process in the hands of data science teams, from prototyping to production, and making the work they do highly visible and shareable. We’re excited to roll out these  features so our clients have the tools they need to really maximize the return on their data science investments.”

 

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