Organizations worldwide are committed to enterprise AI efforts from the top down, but struggle to democratize projects from the bottom up to give more individuals access to actionable data insights. Dataiku 7 brings more people to the table via collaboration and empowers individuals with explainable AI for businesses to use data for day-to-day decisions and build impactful AI projects.
With the launch of Dataiku 7, new features include:
Support for Advanced 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. In the past, advanced statisticians were relegated to siloed tools with no visibility for non-statisticians, creating bottlenecks in governance and AI project deployment.
Advanced Prediction Explanations: Traditionally, machine learning models do not include insights into why or how they arrived at an outcome, making it difficult to objectively explain the decisions made and actions taken based on these models. Prediction explanations in Dataiku open the black box by describing which characteristics, or features, have the greatest impact on a model’s outcomes. Dataiku 7 includes both row-level prediction explanations in output datasets as well as interactive visualizations of individual prediction explanations.
Git for Better Coder Collaboration: With the enhanced Git integration in Dataiku 7, data scientists (or other code-first users) can now create, delete, push, and pull Git branches directly from Dataiku. This brings big efficiency gains, as coders can duplicate projects to easily sandbox changes, leaving the original project unaffected. Once the iteration on the duplicate project is complete, changes can be seamlessly merged back to the original project (with all changes tracked in Git).
More Elasticity With Kubernetes: Dataiku 7 expands on the managed Kubernetes cluster capability from Dataiku 6 by allowing users to now run web apps on Kubernetes clusters. This enables more concurrent users and a fast, flexible execution backend for resource-heavy AI deployments.
A Labeling Plugin for Active Learning: Properly labeled data is a prerequisite for unlocking precise, quality insights from machine learning models and the ability to label data quickly often speeds up the entire analytics lifecycle by easing the tedious and time-consuming data collection step. 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 empowers the Global 2,000 to turn large data sets into actionable insights, democratize AI projects, and massively scale machine learning initiatives. With today's release, Dataiku 7 enhances deeper collaboration and explainable AI to bring data science to more people with its intuitive, team-based platform.
To learn more about today’s announcement and for an in-depth look at Dataiku 7, please
read the release notes.To receive a demo of Dataiku, including a look at the new features in action,
sign up for the webinar on April 16th.
About DataikuDataiku is the centralized data platform that democratizes the use of data science, machine learning, and AI in the enterprise. With Dataiku, businesses are uniquely empowered to move along their data journey from data preparation to analytics at scale to Enterprise AI. By providing a common ground for data experts and explorers, a repository of best practices, shortcuts to machine learning and AI deployment/management, and a centralized, controlled environment, Dataiku is the catalyst for data-powered companies.
View source version on businesswire.com:
https://www.businesswire.com/news/home/20200318005008/en/ContactLaurel Toney
Strange Brew Strategies
dataiku@strangebrewstrategies.comSource : Dataiku
--BERNAMA
