November 22, 2017 - With a range of features aimed at making data science, machine learning, and advanced analytics accessible to organizations as a whole, data science software maker Dataiku has released Dataiku 4.1. The software platform acts as a central hub for technical and non-technical users to prototype, build, scale, deploy, and manage advanced data science products. Building upon the needs of Dataiku customers who have hundreds of users across their organizations around the world relying on the software, Dataiku 4.1 has been designed to accelerate scalable deployment while maintaining its core functionalities such as:
- Point-and-click interfaces for data preparation and analysis
- Customizable tools to facilitate cutting-edge and efficient data science
- Solutions for deploying, monitoring, and governing models in production.
In its latest release, Dataiku is introducing features designed to expand its capabilities as a single platform for everyone, including coders and clickers, spread across any sized organization around the world. Dataiku 4.1 introduces new data preparation “recipes” within the Dataiku graphical interface that bring powerful analytical functionalities to non-coders, including pivoting, sorting, and splitting datasets. For coders, the latest release brings visualization libraries like RShiny and Bokeh for creating engaging interactive web applications within dashboards. Additionally, RMarkdown reports let users share their results outside of Dataiku.
With Dataiku’s “live model competition,” users compare the performance of a batch of machine learning models competing in real time without waiting for the entire training of the model. Additionally, model ensembling, which exploits the strengths of various models by combining different algorithms, is now possible without writing a single line of code. It is common for an organization to have many projects using different versions of Python, R, and libraries. Dataiku 4.1 now supports reproducible environments, which properly isolate projects and reproduce the runtime condition throughout the deployment phase.