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How AI orchestration has become more important than the models themselves

CIO

Large language models (LLMs) just keep getting better. In just about two years since OpenAI jolted the news cycle with the introduction of ChatGPT, weve already seen the launch and subsequent upgrades of dozens of competing models. From Llama3.1 to Gemini to Claude3.5 From Llama3.1 to Gemini to Claude3.5

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5 machine learning essentials nontechnical leaders need to understand

TechCrunch

We’re living in a phenomenal moment for machine learning (ML), what Sonali Sambhus , head of developer and ML platform at Square, describes as “the democratization of ML.” It’s become the foundation of business and growth acceleration because of the incredible pace of change and development in this space.

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The key to operational AI: Modern data architecture

CIO

Recent research shows that 67% of enterprises are using generative AI to create new content and data based on learned patterns; 50% are using predictive AI, which employs machine learning (ML) algorithms to forecast future events; and 45% are using deep learning, a subset of ML that powers both generative and predictive models.

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IDC chief research officer: GenAI, from experimentation to adoption

CIO

As the chief research officer at IDC, I lead a global team of analysts who develop research and provide advice to help our clients navigate the technology landscape. Back in 2023, at the CIO 100 awards ceremony, we were about nine months into exploring generative artificial intelligence (genAI). Build or buy?

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Data engineers vs. data scientists

O'Reilly Media - Data

It’s important to understand the differences between a data engineer and a data scientist. Misunderstanding or not knowing these differences are making teams fail or underperform with big data. I think some of these misconceptions come from the diagrams that are used to describe data scientists and data engineers.

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From legacy to lakehouse: Centralizing insurance data with Delta Lake

CIO

The time-travel functionality of the delta format enables AI systems to access historical data versions for training and testing purposes. Modern AI models, particularly large language models, frequently require real-time data processing capabilities.

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Comprehensive data management for AI: The next-gen data management engine that will drive AI to new heights

CIO

All industries and modern applications are undergoing rapid transformation powered by advances in accelerated computing, deep learning, and artificial intelligence. The next phase of this transformation requires an intelligent data infrastructure that can bring AI closer to enterprise data.