RapidMiner, a data science platform enabling data loving people of all skill levels to rapidly create and operate artificial intelligence (AI) solutions for maximum business impact, announced the latest release of its platform, RapidMiner 9.7. This update prioritizes the team element for successful data science initiatives, continuing to put people at the center of the enterprise AI journey by empowering users of varying backgrounds and abilities.
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As part of this release, RapidMiner Server has dramatically evolved to keep pace with both changing times and changing technology. Now known as the RapidMiner AI Hub, it is built to connect AI to people, processes, systems and value in order to tear down silos and make enterprise AI successful, wide-spread and sustainable. The RapidMiner AI Hub allows people with different tooling preferences and skillsets to collaborate on AI projects within any organization. Creating a shared infrastructure such as this improves costs, agility, data access, transparency, security and compliance.
“As with any digital transformation initiative, successful adoption of data science across an organization requires people to work together efficiently and iteratively,” said Dr. Ingo Mierswa, Founder and CTO of RapidMiner. “Our goal with RapidMiner 9.7 is to facilitate collaboration through a rare combination of agility and traceability in the machine learning (ML) model development lifecycle.”
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RapidMiner 9.7 now offers several new features, such as a new project-based framework that enables unprecedented collaboration and governance of AI initiatives. The framework helps teams convert ideas into models easily and iteratively so they can deliver real business impact. Through AI Hub, users from across the enterprise can work together on ML use cases in a central location, easing collaborative friction.
The projects framework in RapidMiner AI Hub also offers new, fine-grained version control based on Git standards. All changes are tracked, as are the users responsible for said changes, creating a distinct model lineage that helps with collaboration and conflict resolution. The version control provides “snapshots” which enable users to easily roll back to earlier versions of projects, supporting agile AI development.
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