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Gartner: Operational AI Requires Data Engineering, DataOps, and Data-AI Role Alignment

DataKitchen

In this report, Gartner outlines recommendations to effectively operationalize AI solutions that involve the core management competencies of ModelOps, DataOps, and DevOps. Blog: Deliver AI and ML Models at Scale with ModelOps. On-Demand Webinar: Your Model is Not an Island: Operationalize Machine Learning at Scale with ModelOps.

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Trust: The foundation for successful digital transformation

CIO Business Intelligence

The Implications When it comes to establishing trust, organizations currently fall into two very different camps: On one hand, there are organizations in which teams are struggling with unrealistic strategies and manually maintained spreadsheets. These digital operating models help enhance visibility, foster trust, and deliver tangible value.

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Women IT leaders take center stage

CIO Business Intelligence

First, you serve as a role model. Karen Stine, head of data, Willis Towers Watson Willis Towers Watson So Stine focused on raising her profile in the data profession by writing white papers, posting thoughts online, working with professional organizations, seeking speaking engagements, and seizing high-profile opportunities at work. “It

IT 125
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Want AI? Here’s how to get your data and infrastructure AI-ready

CIO Business Intelligence

The key is to make data actionable for AI by implementing a comprehensive data management strategy. Getting the right and optimal responses out of GenAI models requires fine-tuning with industry and company-specific data. That’s because data is often siloed across on-premises, multiple clouds, and at the edge.

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Generative AI will profoundly change healthcare operations

CIO Business Intelligence

EXL predicts that if organizations fully adopt a digital strategy and optimally leverage technology, they could reduce overall administrative expenses by 40% to 50% in the next five years. In addition, large language models can both summarize massive amounts of data and create new, original content.

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Five Ways AI Can Help States Solve Their Hardest Problems (Part 1): Enhance Crisis Response

DataRobot

As a result, policymakers are left without enough time to create and implement targeted, localized strategies in time to prevent outbreaks. On average, DataRobot forecasts had a 21 percent lower rate of error than all other published competing models over a six to eight week period. reporting backlogs). reporting backlogs).

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What Is Model Risk Management and How is it Supported by Enterprise MLOps?

Domino Data Lab

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.