DLT partners with DataRobot to accelerate machine learning and data science initiatives in public sector
DataRobot captures the knowledge, experience and best practices of the world’s leading data scientists, delivering unmatched levels of data science automation and ease-of-use for machine learning initiatives. DataRobot enables government users to build and deploy highly accurate machine learning models in a fraction of the time.
“Identifying and hiring the top data scientists is hard for any organization but none more so than the federal government,” said Erin Hawley, VP of public sector for DataRobot. “Our platform makes it easier than ever for an organization to adopt data science into their environment, at a fraction of the cost and time. We’re excited to partner with DLT as it is their vision to bring together a community of data science partners that will be a key advantage to federal government agencies.”
Recently launched, the DLT AnalyticsStack is a public sector focused Big Data, Analytics and Data Science ‘solution stack’ providing agencies with a scalable reference framework that addresses rapidly evolving big data requirements and use cases. Rather than just piecemealing products, the stack’s ‘building-block’ approach helps agencies quickly architect, procure and deploy enterprise analytics capabilities ranging from big data infrastructure to advanced analytics and data science capabilities. The DLT AnalyticsStack leverages the solution-oriented, ecosystem integration of the stack vendors to help agencies quickly realize the benefits of advanced analytics capabilities.
DataRobot plays a key and foundational role in the DLT AnalyticsStack performing machine learning automation and predictive analytics functions in the stack’s analytics layer. By automating complex machine learning algorithms based in R, Python, H20 and other programming languages, DataRobot quickly creates predictive models helping agencies fully leverage all forms of structured and unstructured data to predict performance, improve situational awareness and simulate potential outcomes critical to mission success.
“To answer new questions of extremely large and complex datasets, agency data scientists’ time is spent mostly on manual data preparation then selection, creation and execution of machine learning algorithms to create high-probability data prediction models,” said David Rubal, chief technologist for data and analytics and principal data scientist at DLT. “By automating these processes, agencies will be able to effectively and fully leverage data to make more informed decisions, predictions and forecasts.”