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Dataiku brings more power to data pros with latest release

By Catherine Knowles
Thu 19 Mar 2020
FYI, this story is more than a year old

Dataiku has launched the latest version of its enterprise AI and machine learning platform, bringing more collaboration and explainability to the offering, with specific features for technical data professionals including statisticians and data scientists.

Key additions for Dataiku 7 are Kubernetes-powered web apps to expand on the capabilities introduced in Dataiku 6 and a machine learning-assisted data labeling plugin, the company states.

According to the company, the new updates are focused on enabling enterprises to democratise projects and give more individuals actionable data insights.

It offers explainable AI for businesses to use data for day to day decisions and help them to build their AI projects.

New features includes support for advanced statistical analysis, advanced prediction explanations, git for better coder collaboration, more abilities with Kubernetes and a labelling plugin for active learning.

Statistical analysis: Statisticians can now use Dataiku to perform advanced statistical analysis in the familiar worksheet-and-cards format while collaborating with the wider data or analytics team. This removes silos and allows visibility for non-statisticians.

Prediction explanations: Dataiku 7 includes both row-level prediction explanations in output datasets as well as interactive visualisations of individual prediction explanations.

Prediction explanations in Dataiku open the black box by describing which characteristics, or features, have the greatest impact on a models outcomes, the company states.

Coder collaboration: With enhanced Git integration in Dataiku 7, data scientists or other code-first users can create, delete, push, and pull Git branches directly from Dataiku.

This means coders can duplicate projects to sandbox changes, leaving the original project unaffected. Once the iteration on the duplicate project is complete, changes can be merged back to the original project with all changes tracked in Git.

Kubernetes elasticity: Dataiku 7 expands on the managed Kubernetes cluster capability from Dataiku 6 by allowing users to now run web apps on Kubernetes clusters. This is particularly useful for resource-heavy AI deployments.

Labelling plugin: The new human-in-the-loop labeling and active learning plugin provides a suite of Dataiku web apps to ease the labeling process whether data is tabular, images, or even sound.

Dataiku CEO Florian Douetteau says, “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 democratise 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 organisations across industries to succeed and understand the impact of their AI model outcomes.”

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