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Dataiku Announces New Version For AI Governance And New Tools For MLOps

Dataiku 10 features a built-in suite of tools to help IT operators and data scientists automatically evaluate, monitor, and compare models under development or in production.

Everyday AI platform Dataiku recently announced new AI governance and oversight as part of the company’s unified AI platform to allow organizations to scale analytics and AI initiatives under one centralized control tower. Dataiku 10 features a built-in suite of tools to help IT operators and data scientists automatically evaluate, monitor, and compare models under development or in production.

Also added in Dataiku 10, organizations can deliver value faster with packaged industry solutions, dedicated workspaces for business users, and accelerators for exploratory data analysis, geospatial analytics, and computer vision. Examples of newly available Dataiku solutions across industries include market basket analysis, product recommendations, plant electricity and CO2 emission forecasting, and real estate pricing, with more in the works.

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“We’ve always believed that to scale AI, organizations need to enlist non-experts to the cause and bring more people into the fold to ensure project success. Dataiku 10 helps make that a reality. This latest version is focused on governance, MLOps, and industry solutions that increase involvement from AI-adjacent roles such as IT operators, risk managers, project managers, and domain experts,” said Clément Stenac, CTO and co-founder at Dataiku.

Dataiku 10 introduces enhancements to its MLOps suite of tools to help IT operators and data scientists evaluate, monitor, and compare ML models, whether under development or in production. Automatic drift analysis and enhanced what-if simulations give teams better visibility into the behaviour and performance of live models. 

AI Governance in Dataiku provides a dedicated watchtower in Dataiku where AI program and project leads, risk managers, and key stakeholders can systematically govern projects and models and oversee progress across the entire AI portfolio. Customers will be able to see all their models in a central model registry, regardless of whether they were developed natively in Dataiku or externally using tools like MLflow. Structured frameworks for project workflows, approvals, and project qualification provide superior AI oversight for enterprise customers. 

Organizations can accelerate speed to value by leveraging our off-the-shelf projects for various vertical use cases that customers can adapt and apply for their own purposes. Examples of advanced tools include new geospatial analytics, native deep learning capabilities, assisted data exploration, and enhanced visual interactive insights.

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Victor Dey
Victor is an aspiring Data Scientist & is a Master of Science in Data Science & Big Data Analytics. He is a Researcher, a Data Science Influencer and also an Ex-University Football Player. A keen learner of new developments in Data Science and Artificial Intelligence, he is committed to growing the Data Science community.

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