Dataiku recently announced the release of Dataiku 9, the latest version of the company’s flagship data science and machine learning platform. The release is highlighted by best practice guardrails to prevent common pitfalls, model assertations to capture and test known use cases, what-if analysis to interactively test model sensitivity, and a new model fairness report to augment existing biased detection methods when building responsible AI models. Dataiku raised $100 million in Series D funding last summer.
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 9 includes a new unified deployer to move projects from design to production environments. The unified deployer makes it easier for business analysts and data scientists to move AI projects from design to production by automating the packaging and deployment to target environments. Programmatic access to the deployment process is also enhanced, allowing for integration with CI/CD systems for project deployment and management.
Version 9 also touts new capabilities for business teams when creating AI projects and consuming AI applications. The release notes add “For business analysts engaged in data preparation tasks, the highly requested fuzzy join recipe makes it easy to join close-but-not-equal columns, an updated formula editor requires less time to learn, and updated date functions simplify time date preparation.” It also touts support for the Dash application framework.
Read Deliver More AI Projects and More Value with Dataiku 9 in the company’s blog.
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