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Are you ready for MLOps? 🫵

Xebia

Gartner reported that on average only 54% of AI models move from pilot to production: Many AI models developed never even reach production. The time when Hardvard Business Review posted the Data Scientist to be the “Sexiest Job of the 21st Century” is more than a decade ago [1]. … that is not an awful lot.

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Unlocking the full potential of enterprise AI

CIO

Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] Reliability and security is paramount.

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When is data too clean to be useful for enterprise AI?

CIO

Once the province of the data warehouse team, data management has increasingly become a C-suite priority, with data quality seen as key for both customer experience and business performance. But along with siloed data and compliance concerns , poor data quality is holding back enterprise AI projects.

Data 211
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Accelerate AWS Well-Architected reviews with Generative AI

AWS Machine Learning - AI

As systems scale, conducting thorough AWS Well-Architected Framework Reviews (WAFRs) becomes even more crucial, offering deeper insights and strategic value to help organizations optimize their growing cloud environments. This allows teams to focus more on implementing improvements and optimizing AWS infrastructure.

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Weights & Biases raises $135M Series C to keep building MLOps software

TechCrunch

ML, or machine learning, is a big market today. In product terms, Weights & Biases plays in the “MLOps” space, or the machine learning operations market. According to Weights & Biases co-founder Lukas Biewald , the software world has a set of tools built for developers to write and deploy code well.

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Lessons learned turning machine learning models into real products and services

O'Reilly Media - Data

Why model development does not equal software development. Today, just 15% of enterprises are using machine learning, but double that number already have it on their roadmaps for the upcoming year. So what should an organization keep in mind before implementing a machine learning solution?

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The future of data: A 5-pillar approach to modern data management

CIO

Digital transformation started creating a digital presence of everything we do in our lives, and artificial intelligence (AI) and machine learning (ML) advancements in the past decade dramatically altered the data landscape. This development will make it easier for smaller organizations to start incorporating AI/ML capabilities.

Data 167