Dataiku has announced the release of Dataiku 7, according to a press release on the company’s website. The update is highlighted by deeper integrations for technical professionals who work on machine learning project development and row-level explainability for white-box AI. There are also new Kubernetes-powered web apps that expand upon functionality released in Dataiku 6, as well as a machine learning-assisted data labeling plugin.
Dataiku offers an advanced analytics solution that allows organizations to create their own data tools. The company’s flagship product features a team-based user interface for both data analysts and data scientists. Dataiku’s unified framework for development and deployment provides immediate access to all the features needed to design data tools from scratch. Users can then apply machine learning and data science techniques to build and deploy predictive data flows.
Dataiku 7 adds support for advanced statistical analysis via the worksheet-and-cards format. New prediction explanations describe which characteristics or features have the greatest impact on a model’s outcomes. The platform includes both row-level prediction explanations in output datasets as well as interactive visualizations of individual explanations as well.
Enhanced Git integration in Dataiku 7 lets data scientists or other technical users create, delete, push, and pull Git branches directly from Dataiku. Once the iteration on the duplicate project is complete, changes can be merged back into the original project (with all the changes tracked in Git). Dataiku users can now also run web apps on Kubernetes clusters, which enables more concurrent users and a flexible backend for resource-heavy AI deployments.
In a media statement about the news, Dataiku CEO Florian Douetteau said: “Collaboration has been at the core of Dataiku since our founding in 2013, and with Dataiku 7, we’re continuing to add features that deepen our philosophy to effectively democratize AI in the enterprise. With this launch, Dataiku 7 is our second consecutive product release that expands features for explainable AI, a critical component for organizations across industries to succeed and understand the impact of their AI model outcomes.”
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