(or 2010s to be more precise) big-data boom brought the emergence of specialization in data roles. What used to be solely described as “Business Intelligence Engineer” was further broken down …
Have you ever felt like your technology is working against you instead of for you? That’s exactly what happens when technical debt piles up. Legacy software requires constant patches, runs …
Image by Author | Ideogram Running multiple large language models can be useful, whether for comparing model outputs, setting up a fallback in case one fails, or customizing behavior …
(lafoto/Shutterstock) The AI revolution has created huge demand for processing power to train frontier models, which Nvidia is filling with its high-end GPUs. But the sudden shift to AI inference …
Microsoft has confirmed that Windows 11 version 25H2 has entered the Windows Insider Release Preview Channel, marking its final testing phase before being released to the general public. The company …
The promise of AI for faster innovation, hyper-personalized customer experiences, and untapped efficiencies feels like a golden ticket. Yet, some companies find themselves tripping over it and fumble to implement …
In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we’re thrilled …
Before we can talk about the new AI corpus, we need to look backward. For decades, data + AI teams have been trained to look downstream towards their analysts or …
Image by Author | ChatGPT Working with data is everywhere now, from small apps to huge systems. But handling data quickly and safely isn’t always easy. That’s where Rust …
(Dave Hoeek/Shutterstock) Gartner says the average company spent around $1.9 million on GenAI last year, yet fewer than 30% of AI leaders think their CEOs are satisfied with the results. …