Remove Machine Learning Remove Metrics Remove Weak Development Team
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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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Boost team productivity with Amazon Q Business Insights

AWS Machine Learning - AI

By monitoring utilization metrics, organizations can quantify the actual productivity gains achieved with Amazon Q Business. Tracking metrics such as time saved and number of queries resolved can provide tangible evidence of the services impact on overall workplace productivity.

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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.

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Responsible AI in action: How Data Reply red teaming supports generative AI safety on AWS

AWS Machine Learning - AI

Red teaming , an adversarial exploit simulation of a system used to identify vulnerabilities that might be exploited by a bad actor, is a crucial component of this effort. Specifically, we discuss Data Replys red teaming solution, a comprehensive blueprint to enhance AI safety and responsible AI practices. What is red teaming?

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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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Gantry launches out of stealth to help data scientists keep AI models fresh

TechCrunch

The demand for AI in the enterprise is insatiable, but the challenge lies in building the support infrastructure and its development and maintenance. “The main challenge in building or adopting infrastructure for machine learning is that the field moves incredibly quickly. Image Credits: Gantry.

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Data trends in 2025

Xebia

Poor data quality automatically results in poor decisions. By 2025, we will place responsibility for the data in the hands of those who know it best: the business teams. Data teams are not known for their empty backlogs, implying a bottleneck for ad-hoc business questions. Lineage (i.e.

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