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Dataiku Paves the Path to Enterprise AI With New Release

Dataiku, recognized as a leading global innovator in data science and advanced analytics, has unveiled Dataiku 5, a new, major upgrade to their software platform that gives data teams the power to build and deploy enterprise AI solutions at scale. The latest version of Dataiku also introduces deep learning functionality on the visual backend and comprehensive documentation features that allows for global, distributed data teams to build powerful applications more efficiently.

"In Dataiku 5 we are unlocking the ability for global teams to build even more powerful AI-driven services," said Florian Douetteau, CEO of Dataiku. "We envision the future of AI application development as a global network of people within an organization, all of different backgrounds and skill levels, contributing to game-changing business applications. We've taken a major step towards that vision in our latest release."

Enterprise AI is on track to change business processes and efficiencies in the near future and Dataiku 5 is the launching point for data teams to begin creating powerful applications all on one single collaborative platform. Dataiku 5 enables scalable Enterprise AI across an organization by introducing a host of new features, namely:

  • Full Containerization with Docker and Kubernetes - ‘Containerized' environments in Dataiku 5 are combined with the power of Docker to go one step further: to in-memory processing of Python & R recipes.
  • Visual Deep Learning with TensorFlow and Keras - Dataiku 5 welcomes Keras and users can now define the architecture of their deep learning models directly from the Visual Machine Learning Interface.
  • Comprehensive Documentation - Users can now share knowledge across the organization with Dataiku Discussions. In addition, organizations can now create Wiki spaces within Dataiku for collaborative creation and editing of documentation.

Currently, hundreds of companies, from SMBs to Fortune 100's use Dataiku on a daily basis to enable their teams to build, deploy, and monitor predictive data flows and build enterprise AI applications that solve industry-wide problems like fraud, churn, supply chain optimization, predictive maintenance, and much more.

Published Wednesday, September 12, 2018 9:27 AM by David Marshall
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