Dataiku Future-Proofs the Path to Enterprise AI with New Fully Managed Kubernetes Cluster Capabilities and White Box AI

Release of Dataiku 6 Delivers Features to Support Elasticity, Sustainable AI, and Cross-Team Collaboration

Dataiku released the latest version of its leading Enterprise AI and machine learning platform, Dataiku 6, which includes the ability for users to easily spin up and manage Kubernetes clusters from inside the Dataiku platform. In addition to elasticity, Dataiku 6 offers a suite of new features to empower organizations to build sustainable AI systems.

Dataiku 6 underscores the company’s continued commitment to helping businesses build AI that stands the test of time, ensuring that they are not hampered by inevitable and unforeseen developments (technological, regulatory, or otherwise) in the space. Other highlights include a new plugin store to more easily expand the power of Dataiku and improved subpopulation analysis for better model performance and avoiding model bias.

“The world of AI is moving incredibly fast, but companies can’t wait for it to slow down in order to get started. That means organizations need to ensure they are future-proofing their approach to AI,” said Florian Douetteau, Dataiku CEO. “Dataiku 6 enables enterprises to do just that by offering more features for white box AI, collaboration, efficiency, and elastic resource management to allow businesses’ AI to evolve along with the technology.”

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Dataiku 6 future-proofs the path to Enterprise AI via:

Elasticity: Dataiku 6 enables users to easily spin up and manage Kubernetes clusters (on AWS, Azure, or GCP) from inside the Dataiku platform. This means that non-admin users can now quickly spin up Kubernetes clusters for optimized execution of Spark or in-memory jobs. Admins can also isolate and manage compute power so that every team gets exactly what they need to run their analysis and deploy Enterprise AI at scale.

In addition, Snowflake users will experience faster runtimes in Dataiku with the new optimized sync with WASB and native execution of Spark jobs in Snowflake. Dataiku 6 also makes it lightning fast to execute long, multi-step SQL data pipelines, allowing for an optimized compute and storage environment when working with SQL data.

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White Box AI: Dataiku 6 has two new visual capabilities (partial dependence plots and subpopulation analysis) that enable users to deep-dive into key aspects of model behavior that can help teams avoid undesirable model biases. With subpopulation analysis, users can easily weed out these unintended model biases and create a more transparent and fair deployment of AI. Meanwhile, partial dependence plots help people understand complex models visually by surfacing the relationship between a feature and the target.

Cross-Team Collaboration and Efficiency: Improved IDE integrations (RStudio, VS Code, SublimeText, PyCharm) enable coders to work in their environment of choice while fueling collaboration on the Dataiku platform. Better visualization is critical for communicating data-driven systems and decisions to business stakeholders as well as for data scientists to understand and track the progress of AI projects. Dataiku 6 makes it seamless to work with external data visualization tools like Qlik and Tableau.

With the collaboration features added in Dataiku 6, data analysts can easily leverage the new plugins store and reuse code created by data engineers and data scientists in their everyday workflows. With features like custom model plugins for visual machine learning and custom charts, coders can now create and share beautiful visuals and custom machine learning models with non-coders.

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