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Make Your Models Matter: What It Takes to Maximize Business Value from Your Machine Learning Initiatives

Cloudera

We are excited by the endless possibilities of machine learning (ML). We recognise that experimentation is an important component of any enterprise machine learning practice. Organizations need to usher their ML models out of the lab (i.e., COPML accounts for the fact that true production machine learning (i.e.,

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How universities are using AI to power operational efficiency

Trigent

The role of technology in the education industry has witnessed some monumental trendsetters, right from 2019, which saw the advent of Big Data , Internet of Things (IoT), and Machine Learning. Artificial Intelligence (AI) has also been a significant contributor, revolutionizing education.

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DataRobot Celebrates a Decade of Innovation with Industry Recognition

DataRobot

This marks a full decade since some of the brightest minds in data science formed DataRobot with a singular vision: to unlock the potential of AI and machine learning for all—for every business, every organization, every industry—everywhere in the world. DataRobot was recognized by Constellation Research on the MLOps Shortlist.

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3 Technical Innovations Ready to Drive Enterprise AI Adoption

IDC

While we’ve discussed the disruptive power that artificial intelligence (AI) applications bring to enterprise organizations, the truth is that AI adoption is still low for these businesses. Automated Machine Learning. Likewise, AI gets embedded in enterprise infrastructure for intelligence and self-management.

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Build Trustworthy AI With MLOps

In our eBook, Building Trustworthy AI with MLOps, we look at how machine learning operations (MLOps) helps companies deliver machine learning applications in production at scale. We also look closely at other areas related to trust, including: AI performance, including accuracy, speed, and stability.

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Not Getting the Most From Your Model Ops? Why Businesses Struggle With Data Science and Machine Learning

TIBCO - Connected Intelligence

Companies have begun to recognize the value of integrating data science (DS) and machine learning (ML) across their organization to reap the benefits of the advanced analytics they can provide. Why are so few organizations able to follow through with model ops adoption? Reading Time: 2 minutes. The Devil’s in the Data.

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Improve Underwriting Using Data and Analytics

Cloudera

To me, this means that by applying more data, analytics, and machine learning to reduce manual efforts helps you work smarter. Simply stated, this approach enables data to be collected from any location and reside in any location for analytics to then be performed. Step two: expand machine learning and AI.

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