Home » Startup » Former Facebook Team Launches Sense Data Science Platform

Former Facebook Team Launches Sense Data Science Platform

San Francisco-based Sense announces the launch of the Sense Data Science Cloud, aiming to power the future of data-driven science and business via its high-productivity analytics platform.

Powered by Docker, Sense’s cloud platform integrates the latest data analysis tools — such as R, Python, and Spark — for the first time into a unified solution to accelerate data science production. Analytical tools such as R can now be loaded in seconds as interactive sessions on high-performance cloud containers or run as part of automated pipelines, allowing data scientists to rapidly build solutions without the need for costly infrastructure or server administration.

Backed by Andrew McCollum, one of the founding team members of Facebook, early Facebook engineer Darian Shirazi, former Microsoft Chief Economist Susan Athey, and Scott Banister — founder of Cisco-acquired IronPort — Sense launches a supercharged cloud platform for data science that allows unparalleled collaboration and ease-of-use, elastic scalability, and rapid development of big data analytics solutions.

With its launch and propelled by its recent funding of $1.2M from Granite Ventures, Illuminate ventures, and a wide range of prominent angels, Sense now aims to bring data science to every organization.

“Sense embraces the massive shift towards open-source technologies and away from proprietary ones, such as Matlab and SAS, to give users an end-to-end platform that accelerates the entire lifecycle of data science from exploration to production,” says Sense Co-founder and CEO Tristan Zajonc, a Harvard University PhD. “Users of Sense can easily build analytics pipelines using the most powerful tools, share their results with others, and deploy their solutions without worrying about infrastructure,” he adds.

Sense is built from the ground up to support rapid-fire analytics of data of any size. Users can instantly scale their analysis without worrying about servers and distribute their analysis beyond a single machine using Sense’s R and Python libraries or integration with Spark and Hadoop.

“Sense radically simplifies building modern data science and Big Data analytics solutions. All the complex details around provisioning compute resources, versioning files, sharing reproducible analyses, orchestrating analytics pipelines, and deploying predictive models are handled by Sense,” says Co-Founder and CTO Anand Patil, a University of California PhD in applied mathematics and statistics. “By taking care of the common challenges faced by data scientists and data-driven enterprises, we can offer a secure end-to-end solution for modern data science that can be easily deployed in the cloud or on-premise,” he explains.

To ensure the security of sensitive data, Sense can be deployed on-premise or within a Virtual Private Cloud for customers who wish to keep their data behind a company-controlled firewall.

“Organizations are starting to realize data science is the key to building a competitive advantage with data,” says CEO Tristan Zajonc. With this in mind, the team at Sense sees the fragmentation that is evident in the analytics ecosystem today as an opportunity. The enterprise data hubs and “data lakes” provided by solutions like Cloudera and Hortonworks are far removed from the visualization and BI tools offered by companies like Tableau and Looker. With Sense, data scientists who are caught in the middle now have the powerful tools to simplify their work in order to build the next-generation of analytics solutions.

According to an Accenture and General Electric study in 2014, 87% of enterprises believe Big Data analytics will redefine the competitive landscape of their industries within the next three years. By adopting Sense, enterprises can take advantage of this shift from collecting and storing data to applying data science and Big Data analytics.

“By making this an easily accessible platform and by taking advantage of recent technologies like Docker, Spark, and cloud computing, we can put a supercomputer optimized for data science at the fingertips of every data scientist and analyst, something that was never before possible,” says Zajonc.

Leave a Reply

Your email address will not be published. Required fields are marked *

*
*