Dataiku DSS 3.1 introduces new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface. Users of all skill levels can now leverage HPE Vertica machine learning, H2O Sparkling Water, MLlib, Scikit-Learn, and XGBoost directly from within the visual analysis section of Dataiku DSS 3.1 to apply powerful machine learning algorithms to their data science projects without having to write a line of code. The blending of visual code-free and free-form code-based transformations is one of the main strengths of Dataiku DSS for the prototyping and production of data applications. In addition to Python, R, SQL, Hive, Impala, and Pig, Dataiku DSS 3.1 now enables Apache Spark users to write transformations and interactive notebooks in Scala, bringing the power of Spark's native and most performant language to data analysts using Dataiku DSS.

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We've covered a lot of low-code/no-code development tools, but they've mostly been relegated to simple app creation, not complicated, cutting-edge technology like machine learning (ML) -- until now. That's because predictive analytics specialist Dataiku has updated its Dataiku Data Science Studio (DSS) platform to Version 3.1, which it said "unleashes visual machine learning." Dataiku has introduced five new back-ends for visual ML development with no need for those hard-to-find, expensive developer types well versed in writing programming code. Much like the numerous mobile development back-ends that take care of tedious chores such as external integrations, database access and so on, those new ML backstops help create predictive ML models. "Dataiku DSS 3.1 introduces new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface," the company said in a statement this week.

Dataiku, the maker of the all-in-one predictive analytics software platform Dataiku Data Science Studio (DSS), has today announced the release of Dataiku DSS 3.1, which now enables transformations in Apache Spark's Scala, adds additional external integrations, an improved UX interface, and includes 5 machine learning engines in its visual analysis section. Dataiku DSS 3.1 introduces new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface. Users of all skill levels can now leverage HPE Vertica machine learning, H2O Sparkling Water, MLlib, Scikit-Learn, and XGBoost directly from within the visual analysis section of Dataiku DSS 3.1 to apply powerful machine learning algorithms to their data science projects without having to write a single line of code. The blending of visual code-free and free-form code-based transformations is one of the main strengths of Dataiku DSS for the prototyping and production of data applications. In addition to Python, R, SQL, Hive, Impala, and Pig, Dataiku DSS 3.1 now enables Apache Spark users to write transformations and interactive notebooks in Scala, bringing the power of Spark's native and most performant language to the data teams using Dataiku DSS.

Join our next free training with Kenji Lefèvre, Product Manager at Dataiku, to understand how Dataiku streamlines the deployment and production of predictive applications with Dataiku 3.0. If the timing is not convenient, register to receive the recording of the session afterwards. Kenji Lefevre is Dataiku's Product Manager, a collaborative platform to design, build, and run predictive applications from start to finish. Before joining Dataiku, Kenji worked as a freelance data scientist. He holds a PhD in mathematics on homotopical algebra and is particularly interested in the popularizing of science.