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How AI orchestration has become more important than the models themselves

CIO

As many companies that have already adopted off-the-shelf GenAI models have found, getting these generic LLMs to work for highly specialized workflows requires a great deal of customization and integration of company-specific data. million on inference, grounding, and data integration for just proof-of-concept AI projects.

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NJ Transit creates ‘data engine’ to fuel transformation

CIO

The chief information and digital officer for the transportation agency moved the stack in his data centers to a best-of-breed multicloud platform approach and has been on a mission to squeeze as much data out of that platform as possible to create the best possible business outcomes. Data engine on wheels’.

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Predibase exits stealth with a low-code platform for building AI models

TechCrunch

Data science teams are stymied by disorganization at their companies, impacting efforts to deploy timely AI and analytics projects. In a recent survey of “data executives” at U.S.-based ” The market for synthetic data is bigger than you think. These are ultimately organizational challenges.

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How to Successfully Implement HR Analytics and People Analytics in a Company

Altexsoft

potential talent is becoming much more “efficient” in many firms, top talent is becoming simultaneously more expensive and more easily lost to competitors,” stresses professor of workforce analytics Mark Huselid in The science and practice of workforce analytics: Introduction to the HRM special issue. . performing and high?potential

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7 data trends on our radar

O'Reilly Media - Ideas

Whether you’re a business leader or a practitioner, here are key data trends to watch and explore in the months ahead. Increasing focus on building data culture, organization, and training. The demand for data skills (“the sexiest job of the 21st century”) hasn’t dissipated.

Trends 109
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Interpreting predictive models with Skater: Unboxing model opacity

O'Reilly Media - Data

Data Scientist Cathy O’Neil has recently written an entire book filled with examples of poor interpretability as a dire warning of the potential social carnage from misunderstood models—e.g., There is also a trade off in balancing a model’s interpretability and its performance.

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Supply Chain Analytics: Opportunities in Data Analysis and Business Intelligence

Altexsoft

diversity of sales channels, complex structure resulting in siloed data and lack of visibility. These challenges can be addressed by intelligent management supported by data analytics and business intelligence (BI) that allow for getting insights from available data and making data-informed decisions to support company development.